ADAPTATION
Analysis
######## Models
mod_nul <- lme4::lmer(fitness ~ 1 + (1|Generation),
weights = N, data = data_sum)
mod_Fruits <- lme4::lmer(fitness ~ Fruit_s + (1|Generation),
weights = N, data = data_sum)
mod_Generation <- lme4::lmer(fitness ~ Generation + (1|Generation),
weights = N, data = data_sum)
mod_Step <- lme4::lmer(fitness~ Step + (1|Generation),
weights = N, data = data_sum)
mod_Lines <- lme4::lmer(fitness~ Line + (1|Generation),
weights = N, data = data_sum)
mod_Generation_Step <- lme4::lmer(fitness~ Step + Generation + (1|Generation),
weights = N, data = data_sum)
mod_Generation_Fruits <- lme4::lmer(fitness ~ Generation + Fruit_s + (1|Generation),
weights = N, data = data_sum)
mod_Fruits_Step <- lme4::lmer(fitness ~ Step + Fruit_s + (1|Generation),
weights = N, data = data_sum)
mod_Generation_Fruits_Step <- lme4::lmer(fitness ~ Generation + Step + Fruit_s + (1|Generation),
weights = N, data = data_sum)
mod_Doubleinteraction <- lme4::lmer(fitness ~ Generation + Step + Fruit_s +
Fruit_s:Generation +
(1|Generation),
weights = N, data = data_sum)
mod_Double_gene <- lme4::lmer(fitness ~ Generation*Fruit_s +
(1|Generation),
weights = N, data = data_sum)
mod_Double <- lme4::lmer(fitness ~ Step*Fruit_s +
(1|Generation),
weights = N, data = data_sum)
mod_Double_Generation <- lme4::lmer(fitness ~ Generation + Step*Fruit_s +
(1|Generation),
weights = N, data = data_sum)
mod_Tripleinteraction <- lme4::lmer(fitness ~ Generation*Step*Fruit_s +
(1|Generation),
weights = N, data = data_sum)
mod_DoublePopulationGeneration <- lme4::lmer(fitness ~ Generation*Line +
(1|Generation),
weights = N, data = data_sum)
# AICC and Loglik
MuMIn::AICc(mod_nul,mod_Fruits,mod_Generation,mod_Step,mod_Lines,mod_Generation_Step,mod_Generation_Fruits,mod_Fruits_Step,mod_Generation_Fruits_Step,mod_Double_gene,mod_Doubleinteraction,mod_Double,mod_Double_Generation,mod_Tripleinteraction,mod_DoublePopulationGeneration)
## df AICc
## mod_nul 3 339.7756
## mod_Fruits 5 333.0398
## mod_Generation 4 334.4473
## mod_Step 5 314.8770
## mod_Lines 31 366.1793
## mod_Generation_Step 6 322.8487
## mod_Generation_Fruits 6 328.4534
## mod_Fruits_Step 7 309.1298
## mod_Generation_Fruits_Step 8 317.0215
## mod_Double_gene 8 342.2655
## mod_Doubleinteraction 10 330.9505
## mod_Double 11 317.2153
## mod_Double_Generation 12 325.1311
## mod_Tripleinteraction 20 354.5837
## mod_DoublePopulationGeneration 60 504.0109
anova(mod_nul,mod_Fruits,mod_Generation,mod_Step,mod_Lines,mod_Generation_Step,mod_Generation_Fruits,mod_Fruits_Step,mod_Generation_Fruits_Step,mod_Doubleinteraction,mod_Double,mod_Double_Generation,mod_Tripleinteraction,mod_DoublePopulationGeneration,mod_Double_gene)
## Data: data_sum
## Models:
## mod_nul: fitness ~ 1 + (1 | Generation)
## mod_Generation: fitness ~ Generation + (1 | Generation)
## mod_Fruits: fitness ~ Fruit_s + (1 | Generation)
## mod_Step: fitness ~ Step + (1 | Generation)
## mod_Generation_Step: fitness ~ Step + Generation + (1 | Generation)
## mod_Generation_Fruits: fitness ~ Generation + Fruit_s + (1 | Generation)
## mod_Fruits_Step: fitness ~ Step + Fruit_s + (1 | Generation)
## mod_Generation_Fruits_Step: fitness ~ Generation + Step + Fruit_s + (1 | Generation)
## mod_Double_gene: fitness ~ Generation * Fruit_s + (1 | Generation)
## mod_Doubleinteraction: fitness ~ Generation + Step + Fruit_s + Fruit_s:Generation +
## mod_Doubleinteraction: (1 | Generation)
## mod_Double: fitness ~ Step * Fruit_s + (1 | Generation)
## mod_Double_Generation: fitness ~ Generation + Step * Fruit_s + (1 | Generation)
## mod_Tripleinteraction: fitness ~ Generation * Step * Fruit_s + (1 | Generation)
## mod_Lines: fitness ~ Line + (1 | Generation)
## mod_DoublePopulationGeneration: fitness ~ Generation * Line + (1 | Generation)
## npar AIC BIC logLik deviance Chisq Df
## mod_nul 3 336.65 347.18 -165.33 330.65
## mod_Generation 4 323.01 337.05 -157.50 315.01 15.6458 1
## mod_Fruits 5 321.74 339.29 -155.87 311.74 3.2688 1
## mod_Step 5 305.39 322.93 -147.69 295.39 16.3510 0
## mod_Generation_Step 6 305.95 327.01 -146.98 293.95 1.4362 1
## mod_Generation_Fruits 6 308.84 329.90 -148.42 296.84 0.0000 0
## mod_Fruits_Step 7 291.48 316.05 -138.74 277.48 19.3583 1
## mod_Generation_Fruits_Step 8 291.95 320.02 -137.97 275.95 1.5325 1
## mod_Double_gene 8 306.40 334.48 -145.20 290.40 0.0000 0
## mod_Doubleinteraction 10 289.56 324.65 -134.78 269.56 20.8451 2
## mod_Double 11 291.19 329.79 -134.59 269.19 0.3679 1
## mod_Double_Generation 12 291.62 333.73 -133.81 267.62 1.5683 1
## mod_Tripleinteraction 20 290.03 360.22 -125.02 250.03 17.5887 8
## mod_Lines 31 304.85 413.64 -121.42 242.85 7.1828 11
## mod_DoublePopulationGeneration 60 322.24 532.80 -101.12 202.24 40.6128 29
## Pr(>Chisq)
## mod_nul
## mod_Generation 7.638e-05 ***
## mod_Fruits 0.07061 .
## mod_Step < 2.2e-16 ***
## mod_Generation_Step 0.23076
## mod_Generation_Fruits 1.00000
## mod_Fruits_Step 1.083e-05 ***
## mod_Generation_Fruits_Step 0.21574
## mod_Double_gene 1.00000
## mod_Doubleinteraction 2.975e-05 ***
## mod_Double 0.54414
## mod_Double_Generation 0.21045
## mod_Tripleinteraction 0.02453 *
## mod_Lines 0.78410
## mod_DoublePopulationGeneration 0.07441 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(mod_Fruits_Step)
## Linear mixed model fit by REML ['lmerMod']
## Formula: fitness ~ Step + Fruit_s + (1 | Generation)
## Data: data_sum
## Weights: N
##
## REML criterion at convergence: 294.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8621 -0.4273 0.0855 0.5085 2.5738
##
## Random effects:
## Groups Name Variance Std.Dev.
## Generation (Intercept) 0.03646 0.1909
## Residual 3.14218 1.7726
## Number of obs: 247, groups: Generation, 24
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.33639 0.12545 -2.681
## Steppool 0.55900 0.16167 3.458
## Stepsecond_postpool 1.00604 0.13160 7.645
## Fruit_sCranberry -0.15931 0.05744 -2.774
## Fruit_sStrawberry -0.26942 0.06258 -4.305
##
## Correlation of Fixed Effects:
## (Intr) Steppl Stpsc_ Frt_sC
## Steppool -0.689
## Stpscnd_pst -0.864 0.670
## Frt_sCrnbrr -0.278 -0.043 -0.005
## Frt_sStrwbr -0.271 -0.036 0.013 0.591
#Posthoc
mod_Best <- lme4::lmer(fitness ~ Step -1 + Fruit_s + (1|Generation),
weights = N, data = data_sum)
emmeans::emmeans(mod_Best, list(pairwise ~ Step:Fruit_s), adjust = "tukey") #
## $`emmeans of Step, Fruit_s`
## Step Fruit_s emmean SE df lower.CL upper.CL
## first_prepool Cherry -0.3364 0.1285 300 -0.694 0.0216
## pool Cherry 0.2226 0.1181 83203 -0.104 0.5493
## second_postpool Cherry 0.6697 0.0678 2115 0.482 0.8574
## first_prepool Cranberry -0.4957 0.1256 287 -0.846 -0.1456
## pool Cranberry 0.0633 0.1115 92051 -0.245 0.3718
## second_postpool Cranberry 0.5103 0.0618 1382 0.339 0.6814
## first_prepool Strawberry -0.6058 0.1271 294 -0.960 -0.2516
## pool Strawberry -0.0468 0.1136 91454 -0.361 0.2672
## second_postpool Strawberry 0.4002 0.0669 1974 0.215 0.5854
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 9 estimates
##
## $`pairwise differences of Step, Fruit_s`
## contrast estimate SE df
## first_prepool Cherry - pool Cherry -0.559 0.1640 797
## first_prepool Cherry - second_postpool Cherry -1.006 0.1347 303
## first_prepool Cherry - first_prepool Cranberry 0.159 0.0575 114310
## first_prepool Cherry - pool Cranberry -0.400 0.1715 920
## first_prepool Cherry - second_postpool Cranberry -0.847 0.1463 407
## first_prepool Cherry - first_prepool Strawberry 0.269 0.0626 112175
## first_prepool Cherry - pool Strawberry -0.290 0.1735 987
## first_prepool Cherry - second_postpool Strawberry -0.737 0.1493 455
## pool Cherry - second_postpool Cherry -0.447 0.1227 12246
## pool Cherry - first_prepool Cranberry 0.718 0.1761 1109
## pool Cherry - pool Cranberry 0.159 0.0575 114310
## pool Cherry - second_postpool Cranberry -0.288 0.1382 17293
## pool Cherry - first_prepool Strawberry 0.828 0.1776 1124
## pool Cherry - pool Strawberry 0.269 0.0626 112175
## pool Cherry - second_postpool Strawberry -0.178 0.1411 19292
## second_postpool Cherry - first_prepool Cranberry 1.165 0.1467 454
## second_postpool Cherry - pool Cranberry 0.606 0.1327 17931
## second_postpool Cherry - second_postpool Cranberry 0.159 0.0575 114310
## second_postpool Cherry - first_prepool Strawberry 1.275 0.1478 457
## second_postpool Cherry - pool Strawberry 0.716 0.1343 18072
## second_postpool Cherry - second_postpool Strawberry 0.269 0.0626 112175
## first_prepool Cranberry - pool Cranberry -0.559 0.1640 797
## first_prepool Cranberry - second_postpool Cranberry -1.006 0.1347 303
## first_prepool Cranberry - first_prepool Strawberry 0.110 0.0545 141545
## first_prepool Cranberry - pool Strawberry -0.449 0.1731 1016
## first_prepool Cranberry - second_postpool Strawberry -0.896 0.1463 439
## pool Cranberry - second_postpool Cranberry -0.447 0.1227 12246
## pool Cranberry - first_prepool Strawberry 0.669 0.1726 962
## pool Cranberry - pool Strawberry 0.110 0.0545 141545
## pool Cranberry - second_postpool Strawberry -0.337 0.1350 18114
## second_postpool Cranberry - first_prepool Strawberry 1.116 0.1444 394
## second_postpool Cranberry - pool Strawberry 0.557 0.1335 16219
## second_postpool Cranberry - second_postpool Strawberry 0.110 0.0545 141545
## first_prepool Strawberry - pool Strawberry -0.559 0.1640 797
## first_prepool Strawberry - second_postpool Strawberry -1.006 0.1347 303
## pool Strawberry - second_postpool Strawberry -0.447 0.1227 12246
## t.ratio p.value
## -3.408 0.0197
## -7.467 <.0001
## 2.773 0.1235
## -2.330 0.3250
## -5.789 <.0001
## 4.303 0.0006
## -1.669 0.7656
## -4.933 <.0001
## -3.644 0.0083
## 4.080 0.0016
## 2.773 0.1235
## -2.083 0.4853
## 4.664 0.0001
## 4.303 0.0006
## -1.259 0.9430
## 7.946 <.0001
## 4.568 0.0002
## 2.773 0.1235
## 8.629 <.0001
## 5.335 <.0001
## 4.303 0.0006
## -3.408 0.0197
## -7.467 <.0001
## 2.020 0.5293
## -2.593 0.1905
## -6.124 <.0001
## -3.644 0.0083
## 3.876 0.0036
## 2.020 0.5293
## -2.496 0.2340
## 7.731 <.0001
## 4.174 0.0010
## 2.020 0.5293
## -3.408 0.0197
## -7.467 <.0001
## -3.644 0.0083
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 9 estimates
emmeans::emmeans(mod_Best, list(pairwise ~ Fruit_s), adjust = "tukey") #
## $`emmeans of Fruit_s`
## Fruit_s emmean SE df lower.CL upper.CL
## Cherry 0.1853 0.0708 1709 0.0161 0.3545
## Cranberry 0.0260 0.0632 1231 -0.1252 0.1772
## Strawberry -0.0841 0.0671 1475 -0.2444 0.0762
##
## Results are averaged over the levels of: Step
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 3 estimates
##
## $`pairwise differences of Fruit_s`
## contrast estimate SE df t.ratio p.value
## Cherry - Cranberry 0.159 0.0575 114310 2.773 0.0154
## Cherry - Strawberry 0.269 0.0626 112175 4.303 <.0001
## Cranberry - Strawberry 0.110 0.0545 141545 2.020 0.1074
##
## Results are averaged over the levels of: Step
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans::emmeans(mod_Best, list(pairwise ~ Step), adjust = "tukey") #
## $`emmeans of Step`
## Step emmean SE df lower.CL upper.CL
## first_prepool -0.4793 0.1226 252 -0.774 -0.185
## pool 0.0797 0.1094 81509 -0.181 0.341
## second_postpool 0.5267 0.0562 970 0.392 0.661
##
## Results are averaged over the levels of: Fruit_s
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 3 estimates
##
## $`pairwise differences of Step`
## contrast estimate SE df t.ratio p.value
## first_prepool - pool -0.559 0.164 797 -3.408 0.0020
## first_prepool - second_postpool -1.006 0.135 303 -7.467 <.0001
## pool - second_postpool -0.447 0.123 12246 -3.644 0.0008
##
## Results are averaged over the levels of: Fruit_s
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
summary(mod_Best)
## Linear mixed model fit by REML ['lmerMod']
## Formula: fitness ~ Step - 1 + Fruit_s + (1 | Generation)
## Data: data_sum
## Weights: N
##
## REML criterion at convergence: 294.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8621 -0.4273 0.0855 0.5085 2.5738
##
## Random effects:
## Groups Name Variance Std.Dev.
## Generation (Intercept) 0.03646 0.1909
## Residual 3.14218 1.7726
## Number of obs: 247, groups: Generation, 24
##
## Fixed effects:
## Estimate Std. Error t value
## Stepfirst_prepool -0.33639 0.12545 -2.681
## Steppool 0.22261 0.11808 1.885
## Stepsecond_postpool 0.66965 0.06728 9.953
## Fruit_sCranberry -0.15931 0.05744 -2.774
## Fruit_sStrawberry -0.26942 0.06258 -4.305
##
## Correlation of Fixed Effects:
## Stpfr_ Steppl Stpsc_ Frt_sC
## Steppool 0.120
## Stpscnd_pst 0.175 0.220
## Frt_sCrnbrr -0.278 -0.355 -0.528
## Frt_sStrwbr -0.271 -0.337 -0.480 0.591
################### BELOW TEMP: A SUPR
############### Percent of Increase in fitness
Calcul_increase_fitness <- expand.grid(Step = unique(data_sum$Step),
Fruit_s = unique(data_sum$Fruit_s))
Calcul_increase_fitness$fitness_predicted <- predict(mod_Best, newdata = Calcul_increase_fitness,
re.form= NA, type = "response")
((Calcul_increase_fitness$fitness_predicted[Calcul_increase_fitness$Step=="second_postpool"]-
Calcul_increase_fitness$fitness_predicted[Calcul_increase_fitness$Step=="first_prepool"])/
abs(Calcul_increase_fitness$fitness_predicted[Calcul_increase_fitness$Step=="first_prepool"]))*100
## [1] 299.0683 202.9538 166.0655
Calcul_increase_fitness
## Step Fruit_s fitness_predicted
## 1 first_prepool Cherry -0.33639290
## 2 second_postpool Cherry 0.66965164
## 3 pool Cherry 0.22261164
## 4 first_prepool Cranberry -0.49570136
## 5 second_postpool Cranberry 0.51034318
## 6 pool Cranberry 0.06330318
## 7 first_prepool Strawberry -0.60581186
## 8 second_postpool Strawberry 0.40023267
## 9 pool Strawberry -0.04680732
Plot
#Position dodge
pd <- ggplot2::position_dodge(0.3) # move them .05 to the left and right
#Extract slope and intercept
dat_predict_allfruits <- expand.grid(Generation =as.numeric(levels(as.factor(data_sum$Generation))),
Fruit_s=unique(data_sum$Fruit_s))
dat_predict_allfruits$Step <- ifelse(dat_predict_allfruits$Generation<= 7, "first_prepool",ifelse(dat_predict_allfruits$Generation>=12, "second_postpool", "pool"))
dat_predict_allfruits$fitness_predicted <- predict(mod_Best, newdata = dat_predict_allfruits,
re.form= NA, type = "response")
#REAL DATA
#Add G1 G6 and G7
TEMP_lineG1 <- c(rep(NA, 2), 1,rep(NA, 5))
TEMP_lineG6 <- c(rep(NA, 2), 6,rep(NA, 5))
TEMP_lineG7 <- c(rep(NA, 2), 7,rep(NA, 5))
TEMP_total <- rbind(data_sum,TEMP_lineG1,TEMP_lineG1,TEMP_lineG1,TEMP_lineG6,TEMP_lineG6,TEMP_lineG6,TEMP_lineG7,TEMP_lineG7,TEMP_lineG7)
TEMP_total$Fruit_s[TEMP_total$Generation == "6"] <- c("Strawberry", "Cranberry", "Cherry")
TEMP_total$Fruit_s[TEMP_total$Generation == "1"] <- c("Strawberry", "Cranberry", "Cherry")
TEMP_total$Fruit_s[TEMP_total$Generation == "7"] <- c("Strawberry", "Cranberry", "Cherry")
tail(TEMP_total)
## Line Fruit_s Generation Step N Nb_adults sd fitness se_fitness
## 251 <NA> Strawberry 6 <NA> NA NA NA NA NA
## 252 <NA> Cranberry 6 <NA> NA NA NA NA NA
## 253 <NA> Cherry 6 <NA> NA NA NA NA NA
## 254 <NA> Strawberry 7 <NA> NA NA NA NA NA
## 255 <NA> Cranberry 7 <NA> NA NA NA NA NA
## 256 <NA> Cherry 7 <NA> NA NA NA NA NA
## Add label
TEMP_anno <- data.frame(x1 = c(3.5, 3.5, 10, 3.5, 3.5, 10, 3.5, 3.5, 10),
x2 = c(9, 17.5, 17.5, 9, 17.5, 17.5, 9, 17.5, 17.5),
y1 = c(1, 2, 1.2, 1, 2, 1.2, 1, 2, 1.2),
y2 = c(1.25, 2.25, 1.45, 1.25, 2.25, 1.45, 1.25, 2.25, 1.45),
xstar = c(6.5, 10, 14, 6.5, 10, 14, 6.5, 10, 14),
ystar = c(1.5, 2.5, 1.7, 1.5, 2.5, 1.7, 1.5, 2.5, 1.7),
lab = c("**", "***", "***", "**", "***", "***", "**", "***", "***"),
Fruit_s = c("Cherry", "Cherry", "Cherry",
"Cranberry", "Cranberry", "Cranberry",
"Strawberry", "Strawberry", "Strawberry"),
Line = NA)
TEMP_anno
## x1 x2 y1 y2 xstar ystar lab Fruit_s Line
## 1 3.5 9.0 1.0 1.25 6.5 1.5 ** Cherry NA
## 2 3.5 17.5 2.0 2.25 10.0 2.5 *** Cherry NA
## 3 10.0 17.5 1.2 1.45 14.0 1.7 *** Cherry NA
## 4 3.5 9.0 1.0 1.25 6.5 1.5 ** Cranberry NA
## 5 3.5 17.5 2.0 2.25 10.0 2.5 *** Cranberry NA
## 6 10.0 17.5 1.2 1.45 14.0 1.7 *** Cranberry NA
## 7 3.5 9.0 1.0 1.25 6.5 1.5 ** Strawberry NA
## 8 3.5 17.5 2.0 2.25 10.0 2.5 *** Strawberry NA
## 9 10.0 17.5 1.2 1.45 14.0 1.7 *** Strawberry NA
TEMP_title <- data.frame(xtitle = c(14, 14, 14),
ytitle = c(3, 3, 3),
title = c("Cherry", "Cranberry", "Strawberry"),
Fruit_s = c("Cherry", "Cranberry", "Strawberry"),
Line = NA)
TEMP_title
## xtitle ytitle title Fruit_s Line
## 1 14 3 Cherry Cherry NA
## 2 14 3 Cranberry Cranberry NA
## 3 14 3 Strawberry Strawberry NA
PLOT_FITNESS_CHERRY <- ggplot(data = TEMP_total[TEMP_total$Fruit_s == "Cherry",],
aes(x = factor(Generation),group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin =fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.2,color = "black") +
geom_line(size = 0.3,position = pd) +
geom_line(data = dat_predict_allfruits[dat_predict_allfruits$Fruit_s == "Cherry",],
aes(x = factor(Generation), y = fitness_predicted,
colour = "black", group = Step), size = 0.5) +
geom_point(size =1, position = pd, shape =21, fill = "white") +
ylim(-3, 3.05) +
ylab("Fitness") +
xlab("Generation") +
geom_text(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = xstar, y = ystar, label = lab), size =3.3) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = x1, xend = x1,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = x2, xend = x2,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = x1, xend = x2,
y = y2, yend = y2), size = 0.4) +
scale_color_manual(values = c("black", "#BC3C6D", "#FDB424", "#3FAA96")) +
ggtitle("Cherry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#BC3C6D"))
PLOT_FITNESS_CHERRY
PLOT_FITNESS_CRANB <- ggplot2::ggplot(data = TEMP_total[TEMP_total$Fruit_s == "Cranberry",],
aes(x = factor(Generation),group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin =fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.2,color = "black") +
geom_line(size = 0.3,position = pd) +
geom_line(data = dat_predict_allfruits[dat_predict_allfruits$Fruit_s == "Cranberry",], aes(x = factor(Generation), y = fitness_predicted, colour = "black", group = Step),
size = 0.5) +
geom_point(size =1, position = pd, shape =21, fill = "white") +
ylim(-3, 3.05) +
ylab("Fitness") +
xlab("Generation") +
geom_text(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = xstar, y = ystar, label = lab), size =3.3) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = x1, xend = x1,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = x2, xend = x2,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = x1, xend = x2,
y = y2, yend = y2), size = 0.4) +
scale_color_manual(values = c("black", "#FDB424")) +
ggtitle("Cranberry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#FDB424"))
PLOT_FITNESS_CRANB
PLOT_FITNESS_STRAW <- ggplot2::ggplot(data = TEMP_total[TEMP_total$Fruit_s == "Strawberry",],
aes(x = factor(Generation),group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin =fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.2,color = "black") +
geom_line(size = 0.3,position = pd) +
geom_line(data = dat_predict_allfruits[dat_predict_allfruits$Fruit_s == "Strawberry",],
aes(x = factor(Generation), y = fitness_predicted, colour = "black", group = Step),
size = 0.5) +
geom_point(size =1, position = pd, shape =21, fill = "white") +
ylim(-3, 3.05) +
ylab("Fitness") +
xlab("Generation") +
geom_text(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = xstar, y = ystar, label = lab), size =3.3) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = x1, xend = x1, y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = x2, xend = x2, y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = x1, xend = x2, y = y2, yend = y2), size = 0.4) +
scale_color_manual(values = c("black", "#3FAA96")) +
ggtitle("Strawberry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#3FAA96"))
PLOT_FITNESS_STRAW
DYNAMIQUE_THREE_JOIN <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_FITNESS_CHERRY + theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0, y = 0.66, width = 1, height = 0.33) +
cowplot::draw_plot(PLOT_FITNESS_CRANB + theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0, y = 0.33, width = 1, height = 0.33) +
cowplot::draw_plot(PLOT_FITNESS_STRAW,
x = 0, y = 00, width = 1, height = 0.33) +
cowplot::draw_plot_label(c("A", "B", "C"),
x = c(0, 0, 0),
y = c(1, 0.66, 0.33),
hjust = c(-0.5, -0.5, -0.5),
vjust = c(1.5, 1.5, 1.5),
size = 12)
DYNAMIQUE_THREE_JOIN
cowplot::save_plot(file =here::here("figures", "FIG_Adaptation.pdf"), DYNAMIQUE_THREE_JOIN, base_height = 17/cm(1), base_width = 11/cm(1), dpi = 1200)
Analyses with indic
#Posthoc
mod_Best <- lme4::lmer(fitness ~ Step -1 + Fruit_s + (1|Generation),
weights = N, data = data_sum)
emmeans::emmeans(mod_Best, list(pairwise ~ Step:Fruit_s), adjust = "tukey") #
## $`emmeans of Step, Fruit_s`
## Step Fruit_s emmean SE df lower.CL upper.CL
## first_prepool Cherry -0.3364 0.1285 300 -0.694 0.0216
## pool Cherry 0.2226 0.1181 83203 -0.104 0.5493
## second_postpool Cherry 0.6697 0.0678 2115 0.482 0.8574
## first_prepool Cranberry -0.4957 0.1256 287 -0.846 -0.1456
## pool Cranberry 0.0633 0.1115 92051 -0.245 0.3718
## second_postpool Cranberry 0.5103 0.0618 1382 0.339 0.6814
## first_prepool Strawberry -0.6058 0.1271 294 -0.960 -0.2516
## pool Strawberry -0.0468 0.1136 91454 -0.361 0.2672
## second_postpool Strawberry 0.4002 0.0669 1974 0.215 0.5854
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 9 estimates
##
## $`pairwise differences of Step, Fruit_s`
## contrast estimate SE df
## first_prepool Cherry - pool Cherry -0.559 0.1640 797
## first_prepool Cherry - second_postpool Cherry -1.006 0.1347 303
## first_prepool Cherry - first_prepool Cranberry 0.159 0.0575 114310
## first_prepool Cherry - pool Cranberry -0.400 0.1715 920
## first_prepool Cherry - second_postpool Cranberry -0.847 0.1463 407
## first_prepool Cherry - first_prepool Strawberry 0.269 0.0626 112175
## first_prepool Cherry - pool Strawberry -0.290 0.1735 987
## first_prepool Cherry - second_postpool Strawberry -0.737 0.1493 455
## pool Cherry - second_postpool Cherry -0.447 0.1227 12246
## pool Cherry - first_prepool Cranberry 0.718 0.1761 1109
## pool Cherry - pool Cranberry 0.159 0.0575 114310
## pool Cherry - second_postpool Cranberry -0.288 0.1382 17293
## pool Cherry - first_prepool Strawberry 0.828 0.1776 1124
## pool Cherry - pool Strawberry 0.269 0.0626 112175
## pool Cherry - second_postpool Strawberry -0.178 0.1411 19292
## second_postpool Cherry - first_prepool Cranberry 1.165 0.1467 454
## second_postpool Cherry - pool Cranberry 0.606 0.1327 17931
## second_postpool Cherry - second_postpool Cranberry 0.159 0.0575 114310
## second_postpool Cherry - first_prepool Strawberry 1.275 0.1478 457
## second_postpool Cherry - pool Strawberry 0.716 0.1343 18072
## second_postpool Cherry - second_postpool Strawberry 0.269 0.0626 112175
## first_prepool Cranberry - pool Cranberry -0.559 0.1640 797
## first_prepool Cranberry - second_postpool Cranberry -1.006 0.1347 303
## first_prepool Cranberry - first_prepool Strawberry 0.110 0.0545 141545
## first_prepool Cranberry - pool Strawberry -0.449 0.1731 1016
## first_prepool Cranberry - second_postpool Strawberry -0.896 0.1463 439
## pool Cranberry - second_postpool Cranberry -0.447 0.1227 12246
## pool Cranberry - first_prepool Strawberry 0.669 0.1726 962
## pool Cranberry - pool Strawberry 0.110 0.0545 141545
## pool Cranberry - second_postpool Strawberry -0.337 0.1350 18114
## second_postpool Cranberry - first_prepool Strawberry 1.116 0.1444 394
## second_postpool Cranberry - pool Strawberry 0.557 0.1335 16219
## second_postpool Cranberry - second_postpool Strawberry 0.110 0.0545 141545
## first_prepool Strawberry - pool Strawberry -0.559 0.1640 797
## first_prepool Strawberry - second_postpool Strawberry -1.006 0.1347 303
## pool Strawberry - second_postpool Strawberry -0.447 0.1227 12246
## t.ratio p.value
## -3.408 0.0197
## -7.467 <.0001
## 2.773 0.1235
## -2.330 0.3250
## -5.789 <.0001
## 4.303 0.0006
## -1.669 0.7656
## -4.933 <.0001
## -3.644 0.0083
## 4.080 0.0016
## 2.773 0.1235
## -2.083 0.4853
## 4.664 0.0001
## 4.303 0.0006
## -1.259 0.9430
## 7.946 <.0001
## 4.568 0.0002
## 2.773 0.1235
## 8.629 <.0001
## 5.335 <.0001
## 4.303 0.0006
## -3.408 0.0197
## -7.467 <.0001
## 2.020 0.5293
## -2.593 0.1905
## -6.124 <.0001
## -3.644 0.0083
## 3.876 0.0036
## 2.020 0.5293
## -2.496 0.2340
## 7.731 <.0001
## 4.174 0.0010
## 2.020 0.5293
## -3.408 0.0197
## -7.467 <.0001
## -3.644 0.0083
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 9 estimates
emmeans::emmeans(mod_Best, list(pairwise ~ Fruit_s), adjust = "tukey") #
## $`emmeans of Fruit_s`
## Fruit_s emmean SE df lower.CL upper.CL
## Cherry 0.1853 0.0708 1709 0.0161 0.3545
## Cranberry 0.0260 0.0632 1231 -0.1252 0.1772
## Strawberry -0.0841 0.0671 1475 -0.2444 0.0762
##
## Results are averaged over the levels of: Step
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 3 estimates
##
## $`pairwise differences of Fruit_s`
## contrast estimate SE df t.ratio p.value
## Cherry - Cranberry 0.159 0.0575 114310 2.773 0.0154
## Cherry - Strawberry 0.269 0.0626 112175 4.303 <.0001
## Cranberry - Strawberry 0.110 0.0545 141545 2.020 0.1074
##
## Results are averaged over the levels of: Step
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans::emmeans(mod_Best, list(pairwise ~ Step), adjust = "tukey") #
## $`emmeans of Step`
## Step emmean SE df lower.CL upper.CL
## first_prepool -0.4793 0.1226 252 -0.774 -0.185
## pool 0.0797 0.1094 81509 -0.181 0.341
## second_postpool 0.5267 0.0562 970 0.392 0.661
##
## Results are averaged over the levels of: Fruit_s
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 3 estimates
##
## $`pairwise differences of Step`
## contrast estimate SE df t.ratio p.value
## first_prepool - pool -0.559 0.164 797 -3.408 0.0020
## first_prepool - second_postpool -1.006 0.135 303 -7.467 <.0001
## pool - second_postpool -0.447 0.123 12246 -3.644 0.0008
##
## Results are averaged over the levels of: Fruit_s
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 3 estimates
summary(mod_Best)
## Linear mixed model fit by REML ['lmerMod']
## Formula: fitness ~ Step - 1 + Fruit_s + (1 | Generation)
## Data: data_sum
## Weights: N
##
## REML criterion at convergence: 294.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8621 -0.4273 0.0855 0.5085 2.5738
##
## Random effects:
## Groups Name Variance Std.Dev.
## Generation (Intercept) 0.03646 0.1909
## Residual 3.14218 1.7726
## Number of obs: 247, groups: Generation, 24
##
## Fixed effects:
## Estimate Std. Error t value
## Stepfirst_prepool -0.33639 0.12545 -2.681
## Steppool 0.22261 0.11808 1.885
## Stepsecond_postpool 0.66965 0.06728 9.953
## Fruit_sCranberry -0.15931 0.05744 -2.774
## Fruit_sStrawberry -0.26942 0.06258 -4.305
##
## Correlation of Fixed Effects:
## Stpfr_ Steppl Stpsc_ Frt_sC
## Steppool 0.120
## Stpscnd_pst 0.175 0.220
## Frt_sCrnbrr -0.278 -0.355 -0.528
## Frt_sStrwbr -0.271 -0.337 -0.480 0.591
############## 0 Add indic
data_sum$indic_phase1 <- ifelse(data_sum$Step=="first_prepool", 1, 0)
data_sum$indic_phase2 <- ifelse(data_sum$Step=="pool", 1, 0)
data_sum$indic_phase3 <- ifelse(data_sum$Step=="second_postpool", 1, 0)
data_sum$indic_cherry<- ifelse(data_sum$Fruit_s=="Cherry", "Cherry", "Others")
data_sum$indic_cranberry <- ifelse(data_sum$Fruit_s=="Cranberry", "Cranberry", "Others")
data_sum$indic_strawberry <- ifelse(data_sum$Fruit_s=="Strawberry", "Strawberry", "Others")
############## 1 Test indic sur step * Fruit
mod_Fruit_Indic1 <- lme4::lmer(fitness ~ Fruit_s*indic_phase1 + (1|Generation),
weights = N, data = data_sum)
mod_Fruit_Indic2 <- lme4::lmer(fitness ~ Fruit_s*indic_phase2 + (1|Generation),
weights = N, data = data_sum)
mod_Fruit_Indic3 <- lme4::lmer(fitness ~ Fruit_s*indic_phase3 + (1|Generation),
weights = N, data = data_sum)
############## 2 Test indic sur fRuit * Fruit
mod_Cherry_Step <- lme4::lmer(fitness ~ Step*indic_cherry + (1|Generation),
weights = N, data = data_sum)
mod_Cranberry_Step <- lme4::lmer(fitness ~ Step*indic_cranberry + (1|Generation),
weights = N, data = data_sum)
mod_Strawberry_Step <- lme4::lmer(fitness ~ Step*indic_strawberry + (1|Generation),
weights = N, data = data_sum)
############## 3 Test indic sur fRuit * Test indic phase 3
mod_Cherry_indic1 <- lme4::lmer(fitness ~ indic_phase1*indic_cherry + (1|Generation),
weights = N, data = data_sum)
mod_Cherry_indic2 <- lme4::lmer(fitness ~ indic_phase2*indic_cherry + (1|Generation),
weights = N, data = data_sum)
mod_Cherry_indic3 <- lme4::lmer(fitness ~ indic_phase3*indic_cherry + (1|Generation),
weights = N, data = data_sum)
MuMIn::AICc(mod_Fruit_Indic1,mod_Fruit_Indic2,mod_Fruit_Indic3,
mod_Cherry_Step,mod_Cranberry_Step,mod_Strawberry_Step,
mod_Cherry_indic1,mod_Cherry_indic2,mod_Cherry_indic3)
## df AICc
## mod_Fruit_Indic1 8 314.8545
## mod_Fruit_Indic2 8 342.1753
## mod_Fruit_Indic3 8 321.9248
## mod_Cherry_Step 8 307.8003
## mod_Cranberry_Step 8 328.4648
## mod_Strawberry_Step 8 316.4444
## mod_Cherry_indic1 6 309.9783
## mod_Cherry_indic2 6 336.5756
## mod_Cherry_indic3 6 315.4998
anova(mod_Fruit_Indic1,mod_Fruit_Indic2,mod_Fruit_Indic3,
mod_Cherry_Step,mod_Cranberry_Step,mod_Strawberry_Step,
mod_Cherry_indic1,mod_Cherry_indic2,mod_Cherry_indic3)
## Data: data_sum
## Models:
## mod_Cherry_indic1: fitness ~ indic_phase1 * indic_cherry + (1 | Generation)
## mod_Cherry_indic2: fitness ~ indic_phase2 * indic_cherry + (1 | Generation)
## mod_Cherry_indic3: fitness ~ indic_phase3 * indic_cherry + (1 | Generation)
## mod_Fruit_Indic1: fitness ~ Fruit_s * indic_phase1 + (1 | Generation)
## mod_Fruit_Indic2: fitness ~ Fruit_s * indic_phase2 + (1 | Generation)
## mod_Fruit_Indic3: fitness ~ Fruit_s * indic_phase3 + (1 | Generation)
## mod_Cherry_Step: fitness ~ Step * indic_cherry + (1 | Generation)
## mod_Cranberry_Step: fitness ~ Step * indic_cranberry + (1 | Generation)
## mod_Strawberry_Step: fitness ~ Step * indic_strawberry + (1 | Generation)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## mod_Cherry_indic1 6 297.91 318.96 -142.95 285.91
## mod_Cherry_indic2 6 326.22 347.28 -157.11 314.22 0.0000 0 1.000000
## mod_Cherry_indic3 6 302.24 323.29 -145.12 290.24 23.9844 0 < 2.2e-16
## mod_Fruit_Indic1 8 296.71 324.79 -140.36 280.71 9.5266 2 0.008537
## mod_Fruit_Indic2 8 325.31 353.38 -154.65 309.31 0.0000 0 1.000000
## mod_Fruit_Indic3 8 301.90 329.98 -142.95 285.90 23.4071 0 < 2.2e-16
## mod_Cherry_Step 8 290.29 318.37 -137.15 274.29 11.6067 0 < 2.2e-16
## mod_Cranberry_Step 8 310.11 338.19 -147.06 294.11 0.0000 0 1.000000
## mod_Strawberry_Step 8 298.37 326.45 -141.19 282.37 11.7394 0 < 2.2e-16
##
## mod_Cherry_indic1
## mod_Cherry_indic2
## mod_Cherry_indic3 ***
## mod_Fruit_Indic1 **
## mod_Fruit_Indic2
## mod_Fruit_Indic3 ***
## mod_Cherry_Step ***
## mod_Cranberry_Step
## mod_Strawberry_Step ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Posthoc
mod_Best <- lme4::lmer(fitness ~ Step*indic_cherry + (1|Generation),
weights = N, data = data_sum)
emmeans::emmeans(mod_Best, list(pairwise ~ Step:indic_cherry), adjust = "tukey") #
## $`emmeans of Step, indic_cherry`
## Step indic_cherry emmean SE df lower.CL upper.CL
## first_prepool Cherry -0.607592 0.1631 674 -1.038 -0.177
## pool Cherry 0.321562 0.1674 111144 -0.119 0.762
## second_postpool Cherry 0.701026 0.0712 2598 0.514 0.889
## first_prepool Others -0.442650 0.1288 340 -0.783 -0.102
## pool Others 0.000256 0.1106 107399 -0.291 0.291
## second_postpool Others 0.455445 0.0585 1121 0.301 0.610
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: sidak method for 6 estimates
##
## $`pairwise differences of Step, indic_cherry`
## contrast estimate SE df t.ratio
## first_prepool Cherry - pool Cherry -0.929 0.2337 2618 -3.976
## first_prepool Cherry - second_postpool Cherry -1.309 0.1779 796 -7.354
## first_prepool Cherry - first_prepool Others -0.165 0.1484 27353 -1.111
## first_prepool Cherry - pool Others -0.608 0.1970 1380 -3.085
## first_prepool Cherry - second_postpool Others -1.063 0.1733 710 -6.135
## pool Cherry - second_postpool Cherry -0.379 0.1819 38241 -2.086
## pool Cherry - first_prepool Others 0.764 0.2112 2298 3.619
## pool Cherry - pool Others 0.321 0.1479 504858 2.173
## pool Cherry - second_postpool Others -0.134 0.1773 34407 -0.755
## second_postpool Cherry - first_prepool Others 1.144 0.1471 475 7.773
## second_postpool Cherry - pool Others 0.701 0.1315 17346 5.328
## second_postpool Cherry - second_postpool Others 0.246 0.0614 125672 3.997
## first_prepool Others - pool Others -0.443 0.1697 999 -2.609
## first_prepool Others - second_postpool Others -0.898 0.1414 401 -6.350
## pool Others - second_postpool Others -0.455 0.1251 13913 -3.638
## p.value
## 0.0010
## <.0001
## 0.8770
## 0.0253
## <.0001
## 0.2946
## 0.0041
## 0.2505
## 0.9748
## <.0001
## <.0001
## 0.0009
## 0.0959
## <.0001
## 0.0037
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 6 estimates
summary(mod_Best)
## Linear mixed model fit by REML ['lmerMod']
## Formula: fitness ~ Step * indic_cherry + (1 | Generation)
## Data: data_sum
## Weights: N
##
## REML criterion at convergence: 291.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7742 -0.4669 0.1121 0.5198 2.5009
##
## Random effects:
## Groups Name Variance Std.Dev.
## Generation (Intercept) 0.03638 0.1907
## Residual 3.11306 1.7644
## Number of obs: 247, groups: Generation, 24
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -0.6076 0.1604 -3.788
## Steppool 0.9292 0.2318 4.008
## Stepsecond_postpool 1.3086 0.1753 7.465
## indic_cherryOthers 0.1649 0.1483 1.112
## Steppool:indic_cherryOthers -0.4862 0.2094 -2.322
## Stepsecond_postpool:indic_cherryOthers -0.4105 0.1605 -2.557
##
## Correlation of Fixed Effects:
## (Intr) Steppl Stpsc_ indc_O Stp:_O
## Steppool -0.692
## Stpscnd_pst -0.915 0.633
## indc_chrryO -0.670 0.464 0.613
## Stppl:ndc_O 0.475 -0.716 -0.434 -0.708
## Stpscnd_:_O 0.619 -0.428 -0.663 -0.924 0.654
Plot with indic
#Position dodge
pd <- ggplot2::position_dodge(0.3) # move them .05 to the left and right
#Extract slope and intercept
dat_predict_allfruits <- expand.grid(Generation =as.numeric(levels(as.factor(data_sum$Generation))),
Fruit_s=unique(data_sum$Fruit_s))
dat_predict_allfruits$Step <- ifelse(dat_predict_allfruits$Generation<= 7, "first_prepool",
ifelse(dat_predict_allfruits$Generation>=12, "second_postpool", "pool"))
dat_predict_allfruits$indic_cherry<- ifelse(dat_predict_allfruits$Fruit_s=="Cherry", "Cherry", "Others")
dat_predict_allfruits$fitness_predicted <- predict(mod_Best, newdata = dat_predict_allfruits,
re.form= NA, type = "response")
#REAL DATA
#Add G1 G6 and G7
TEMP_lineG1 <- c(rep(NA, 2), 1,rep(NA, 5))
TEMP_lineG6 <- c(rep(NA, 2), 6,rep(NA, 5))
TEMP_lineG7 <- c(rep(NA, 2), 7,rep(NA, 5))
TEMP_total <- rbind(data_sum,TEMP_lineG1,TEMP_lineG1,TEMP_lineG1,TEMP_lineG6,TEMP_lineG6,TEMP_lineG6,TEMP_lineG7,TEMP_lineG7,TEMP_lineG7)
TEMP_total$Fruit_s[TEMP_total$Generation == "6"] <- c("Strawberry", "Cranberry", "Cherry")
TEMP_total$Fruit_s[TEMP_total$Generation == "1"] <- c("Strawberry", "Cranberry", "Cherry")
TEMP_total$Fruit_s[TEMP_total$Generation == "7"] <- c("Strawberry", "Cranberry", "Cherry")
tail(TEMP_total)
## Line Fruit_s Generation Step N Nb_adults sd fitness se_fitness
## 251 <NA> Strawberry 6 <NA> NA NA NA NA NA
## 252 <NA> Cranberry 6 <NA> NA NA NA NA NA
## 253 <NA> Cherry 6 <NA> NA NA NA NA NA
## 254 <NA> Strawberry 7 <NA> NA NA NA NA NA
## 255 <NA> Cranberry 7 <NA> NA NA NA NA NA
## 256 <NA> Cherry 7 <NA> NA NA NA NA NA
## indic_phase1 indic_phase2 indic_phase3 indic_cherry indic_cranberry
## 251 NA 6 NA <NA> <NA>
## 252 NA 6 NA <NA> <NA>
## 253 NA 6 NA <NA> <NA>
## 254 NA 7 NA <NA> <NA>
## 255 NA 7 NA <NA> <NA>
## 256 NA 7 NA <NA> <NA>
## indic_strawberry
## 251 <NA>
## 252 <NA>
## 253 <NA>
## 254 <NA>
## 255 <NA>
## 256 <NA>
## Add label
TEMP_anno <- data.frame(x1 = c(3.5, 3.5, 3.5, 10, 3.5, 10),
x2 = c(9, 17.5, 17.5, 17.5, 17.5, 17.5),
y1 = c(1, 2, 2, 1.2, 2, 1.2),
y2 = c(1.25, 2.25, 2.25, 1.45, 2.25, 1.45),
xstar = c(6.5, 10, 10, 14, 10, 14),
ystar = c(1.5, 2.5, 2.5, 1.7, 2.5, 1.7),
lab = c("**", "***", "***", "**", "***", "**"),
Fruit_s = c("Cherry", "Cherry",
"Cranberry", "Cranberry",
"Strawberry", "Strawberry"),
Line = NA)
TEMP_anno
## x1 x2 y1 y2 xstar ystar lab Fruit_s Line
## 1 3.5 9.0 1.0 1.25 6.5 1.5 ** Cherry NA
## 2 3.5 17.5 2.0 2.25 10.0 2.5 *** Cherry NA
## 3 3.5 17.5 2.0 2.25 10.0 2.5 *** Cranberry NA
## 4 10.0 17.5 1.2 1.45 14.0 1.7 ** Cranberry NA
## 5 3.5 17.5 2.0 2.25 10.0 2.5 *** Strawberry NA
## 6 10.0 17.5 1.2 1.45 14.0 1.7 ** Strawberry NA
TEMP_title <- data.frame(xtitle = c(14, 14, 14),
ytitle = c(3, 3, 3),
title = c("Cherry", "Cranberry", "Strawberry"),
Fruit_s = c("Cherry", "Cranberry", "Strawberry"),
Line = NA)
TEMP_title
## xtitle ytitle title Fruit_s Line
## 1 14 3 Cherry Cherry NA
## 2 14 3 Cranberry Cranberry NA
## 3 14 3 Strawberry Strawberry NA
PLOT_FITNESS_CHERRY <- ggplot(data = TEMP_total[TEMP_total$Fruit_s == "Cherry",],
aes(x = factor(Generation),group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin =fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.2,color = "black") +
geom_line(size = 0.3,position = pd) +
geom_line(data = dat_predict_allfruits[dat_predict_allfruits$Fruit_s == "Cherry",],
aes(x = factor(Generation), y = fitness_predicted,
colour = "black", group = Step), size = 0.5) +
geom_point(size =1, position = pd, shape =21, fill = "white") +
ylim(-3, 3.05) +
ylab("Fitness") +
xlab("Generation") +
geom_text(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = xstar, y = ystar, label = lab), size =3.3) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = x1, xend = x1,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = x2, xend = x2,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cherry",], aes(x = x1, xend = x2,
y = y2, yend = y2), size = 0.4) +
scale_color_manual(values = c("black", "#BC3C6D", "#FDB424", "#3FAA96")) +
ggtitle("Cherry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#BC3C6D"))
PLOT_FITNESS_CHERRY
PLOT_FITNESS_CRANB <- ggplot2::ggplot(data = TEMP_total[TEMP_total$Fruit_s == "Cranberry",],
aes(x = factor(Generation),group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin =fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.2,color = "black") +
geom_line(size = 0.3,position = pd) +
geom_line(data = dat_predict_allfruits[dat_predict_allfruits$Fruit_s == "Cranberry",], aes(x = factor(Generation), y = fitness_predicted, colour = "black", group = Step),
size = 0.5) +
geom_point(size =1, position = pd, shape =21, fill = "white") +
ylim(-3, 3.05) +
ylab("Fitness") +
xlab("Generation") +
geom_text(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = xstar, y = ystar, label = lab), size =3.3) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = x1, xend = x1,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = x2, xend = x2,
y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Cranberry",], aes(x = x1, xend = x2,
y = y2, yend = y2), size = 0.4) +
scale_color_manual(values = c("black", "#FDB424")) +
ggtitle("Cranberry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#FDB424"))
PLOT_FITNESS_CRANB
PLOT_FITNESS_STRAW <- ggplot2::ggplot(data = TEMP_total[TEMP_total$Fruit_s == "Strawberry",],
aes(x = factor(Generation),group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin =fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.2,color = "black") +
geom_line(size = 0.3,position = pd) +
geom_line(data = dat_predict_allfruits[dat_predict_allfruits$Fruit_s == "Strawberry",],
aes(x = factor(Generation), y = fitness_predicted, colour = "black", group = Step),
size = 0.5) +
geom_point(size =1, position = pd, shape =21, fill = "white") +
ylim(-3, 3.05) +
ylab("Fitness") +
xlab("Generation") +
geom_text(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = xstar, y = ystar, label = lab), size =3.3) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = x1, xend = x1, y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = x2, xend = x2, y = y1, yend = y2), size = 0.4) +
geom_segment(data = TEMP_anno[TEMP_anno$Fruit_s == "Strawberry",],
aes(x = x1, xend = x2, y = y2, yend = y2), size = 0.4) +
scale_color_manual(values = c("black", "#3FAA96")) +
ggtitle("Strawberry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#3FAA96"))
PLOT_FITNESS_STRAW
DYNAMIQUE_THREE_JOIN <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_FITNESS_CHERRY + theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0, y = 0.66, width = 1, height = 0.33) +
cowplot::draw_plot(PLOT_FITNESS_CRANB + theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0, y = 0.33, width = 1, height = 0.33) +
cowplot::draw_plot(PLOT_FITNESS_STRAW,
x = 0, y = 00, width = 1, height = 0.33) +
cowplot::draw_plot_label(c("A", "B", "C"),
x = c(0, 0, 0),
y = c(1, 0.66, 0.33),
hjust = c(-0.5, -0.5, -0.5),
vjust = c(1.5, 1.5, 1.5),
size = 12)
DYNAMIQUE_THREE_JOIN
cowplot::save_plot(file =here::here("figures", "FIG_TEMP_Adaptation_Indic.pdf"), DYNAMIQUE_THREE_JOIN, base_height = 17/cm(1), base_width = 11/cm(1), dpi = 1200)
HETEROGENEITY
Plot
pd <- position_dodge(width = 0.5)
################### INTERMEDIATE PHENOTYPING
# Re-order levels of Line
data_sum_G7 <- data_logchange[data_logchange$Generation == "7",]
data_sum_G7$Line <- factor(data_sum_G7$Line, levels= c("CE2", "CE1", "CE4", "CE3",
"CR2", "CR3", "CR5", "CR1", "CR4",
"FR2", "FR3", "FR5", "FR1", "FR4"))
#Plot:
symp_allop_g7 <- ggplot(data = data_sum_G7,
aes(x = Line, y = logchange, group = Treatment,
color = Fruit_s, shape = Treatment, fill = Fruit_s)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey", size = 0.5) +
geom_errorbar(aes(ymin = logchange-1.96*sd_logchange,
ymax =logchange + 1.96*sd_logchange, linetype = Line_type),
width = 0.2, size = 0.5, alpha = 0.6,position = pd) +
geom_point(position = pd, fill = "white", size =3) +
ylab("Fitness difference between intermediate\nand initial phenotyping steps") +
xlab("Populations") +
labs(shape = "Test fruit", color = "Selection fruit") +
guides(color = FALSE,
shape = guide_legend(override.aes = list(fill = c("black")))) +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_fill_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober + theme(axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-2, 2), xlim=c(0, 15),clip = "off") +
ggtitle("Intermediate phenotyping step") +
annotate("text", x = 2.5, y =1.5, label = 'bold(" Evolved\non cherry")',
size =3,colour = "#BC3C6D",parse = TRUE) +
annotate("text", x = 7.5, y =1.5, label = 'bold(" Evolved\non cranberry")',
size =3,colour = "#FDB424",parse = TRUE) +
annotate("text", x = 12.5, y =1.5, label = 'bold(" Evolved\non strawberry")',
size =3,colour = "#3FAA96",parse = TRUE) +
geom_segment(x = 15.5, y = -0.1, xend= 15.5, yend = -1.2, size = 0.15,
arrow = arrow(length = unit(0.05, "npc")),colour = "black") +
geom_segment(x = 15.5, y = 0.1, xend= 15.5, yend = 1.2, size = 0.15,
arrow = arrow(length = unit(0.05, "npc")),colour = "black") +
annotate("text", x = 16.2, y = 0.5, label = 'bold(" Fitness\nincrease")',
size =3,colour = "black",parse = TRUE, angle =90) +
annotate("text", x = 16.2, y =-0.5, label = 'bold(" Fitness\ndecrease")',
size =3,colour = "black",parse = TRUE, angle =90)
symp_allop_g7
################### FINAL PHENOTYPING
# Re-order levels of Line
data_sum_G29 <- data_logchange[data_logchange$Generation == "29",]
data_sum_G29$Line <- factor(data_sum_G29$Line, levels = c("CEA", "CEB", "CEC",
"CRD", "CRA", "CRC", "CRB", "CRE",
"FRA", "FRC", "FRB"))
symp_allop_G29 <- ggplot(data = data_sum_G29,
aes(x = Line, y=logchange, group = Treatment,
color =Fruit_s, shape = Treatment,fill =Fruit_s)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey", size = 0.5) +
geom_point(size =3, position = pd) +
geom_errorbar(aes(ymin =logchange-1.96*sd_logchange, ymax =logchange + 1.96*sd_logchange),
width= 0.2, size = 0.5, alpha = 0.6,position = pd) +
ylab("Fitness difference between final\nand initial phenotyping steps") +
xlab("Populations") +
labs(shape = "Test fruit", color = "Selection fruit") +
guides(fill = FALSE, alpha = FALSE) +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_fill_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober + theme(axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-2, 2), xlim=c(0, 11),clip = "off") +
annotate("text", x = 2, y =1.5, label = 'bold(" Evolved\non cherry")',
size =3,colour = "#BC3C6D",parse = TRUE) +
annotate("text", x = 6, y =1.5, label = 'bold(" Evolved\non cranberry")',
size =3,colour = "#FDB424",parse = TRUE) +
annotate("text", x = 10, y =1.5, label = 'bold(" Evolved\non strawberry")',
size =3,colour = "#3FAA96",parse = TRUE) +
geom_segment(x = 11.5, y = -0.1, xend= 11.5, yend = -1.2, size = 0.15,
arrow = arrow(length = unit(0.05, "npc")),colour = "black") +
geom_segment(x = 11.5, y = 0.1, xend= 11.5, yend = 1.2, size = 0.15,
arrow = arrow(length = unit(0.05, "npc")),colour = "black") +
annotate("text", x = 12, y = 0.5, label = 'bold(" Fitness\nincrease")',
size =3,colour = "black",parse = TRUE, angle =90) +
annotate("text", x = 12, y = -0.5, label = 'bold(" Fitness\ndecrease")',
size =3,colour = "black", parse = TRUE, angle =90) +
ggtitle("Final phenotyping step")
symp_allop_G29
### Add stroke
DIF_FITNESS_G7 <- symp_allop_g7 +
geom_point(aes(alpha = SA, color = interaction(SA,Fruit_s)), position = pd, size =3, stroke =1.5, fill = "white") +
scale_alpha_manual(values = c(0, 1)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96", "#7C2748", "#CA8702", "#328677", "#BC3C6D", "#FDB424", "#3FAA96"))
DIF_FITNESS_G7
DIF_FITNESS_G29 <- symp_allop_G29 + geom_point(aes(alpha = SA, color =interaction(SA,Fruit_s)), position = pd, size =3, stroke =1.5) +
scale_alpha_manual(values = c(0, 1)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96", "#7C2748", "#CA8702", "#328677", "#BC3C6D", "#FDB424", "#3FAA96"))
DIF_FITNESS_G29
plot_legend <- ggplot(data = data_logchange, aes(x = Line, y=logchange, group = Treatment,
color =Fruit_s, shape = Treatment,fill =Fruit_s)) +
geom_point(size =3, position = pd) +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(21, 22, 24)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober +
guides(fill = FALSE) +
theme(legend.key.size = unit(0.5, "cm"),
axis.text.x = element_blank()) +
coord_cartesian(expand = FALSE, ylim = c(-2, 2), xlim=c(0, 11),clip = "off")
###########################################################
#############################################ALL PLOT
legend_trade <- lemon::g_legend(plot_legend)
SYMP_ALLOP_TOTAL <- cowplot::ggdraw() +
cowplot::draw_plot(DIF_FITNESS_G7 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm")),
x = 0, y = 0.5, width = 1, height = 0.5) +
cowplot::draw_plot(DIF_FITNESS_G29 + theme(legend.position = 'none',
plot.margin =unit(c(0.5, 3.5, 0.5, 0.5), "cm")),
x = 0, y = 0, width = 1, height = 0.5) +
cowplot::draw_plot_label(c("A", "B"),
x = c(0, 0), y = c(1, 0.5),
size = 14) +
cowplot::draw_plot(legend_trade, x = 0.93, y = 0.5, width = 0.001, height = 0.001)
SYMP_ALLOP_TOTAL
cowplot::save_plot(file =here::here("figures", "FIG_Heterogeneity.pdf"),
SYMP_ALLOP_TOTAL, base_height = 20/cm(1), base_width = 20/cm(1), dpi = 1200)
MA-AXIS REGRESSION
Analysis: a supr
# ##### Create empty dataframe with slopes and confidence interval for all pairwise
Estimates_pairwise <- data.frame(Variables = rep(rep(c("slope", "slope_CI_inf", "slope_CI_sup",
"intercept", "intercept_CI_inf", "intercept_CI_sup"),2),3),
Generation = rep(c(rep(7, 6), rep(29, 6)),3),
Pairwise = rep(c("Cherry_Cranberry",
"Cranberry_Strawberry",
"Strawberry_Cherry"), each=12),
Estimates = NA)
#########################
######################### G7
#########################
#######
####### Cherry Cranberry
#######
mod0_G7_CheCran <- lmodel2::lmodel2(logchange_allop ~ logchange_symp,
range.y = "interval",range.x = "interval",
data = TEMP_dataG7_CheCran, nperm=1000)
mod0_G7_CheCran
##
## Model II regression
##
## Call: lmodel2::lmodel2(formula = logchange_allop ~ logchange_symp, data
## = TEMP_dataG7_CheCran, range.y = "interval", range.x = "interval",
## nperm = 1000)
##
## n = 9 r = 0.52317 r-square = 0.2737069
## Parametric P-values: 2-tailed = 0.1483644 1-tailed = 0.07418221
## Angle between the two OLS regression lines = 30.10181 degrees
##
## Permutation tests of OLS, MA, RMA slopes: 1-tailed, tail corresponding to sign
## A permutation test of r is equivalent to a permutation test of the OLS slope
## P-perm for SMA = NA because the SMA slope cannot be tested
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 0.015811976 0.9709274 44.15491 0.09190809
## 2 MA -0.034413319 2.8662681 70.76685 0.09190809
## 3 SMA -0.007638014 1.8558544 61.68264 NA
## 4 RMA -0.011960209 2.0189601 63.65058 0.09190809
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS -0.37501980 0.40664375 -0.4426285 2.384483
## 2 MA 0.23775279 0.01609149 0.9603793 -7.404403
## 3 SMA -0.05773723 0.01717939 0.9193256 3.746437
## 4 RMA 0.48011317 0.03818908 0.1264877 -16.550302
##
## Eigenvalues: 0.3223501 0.05634676
##
## H statistic used for computing C.I. of MA: 0.2050449
#Value:
mod0_G7_CheCran$regression.results[2,]
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 2 MA -0.03441332 2.866268 70.76685 0.09190809
mod0_G7_CheCran$confidence.intervals[2,]
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 2 MA 0.2377528 0.01609149 0.9603793 -7.404403
#Save slope, intercept and ci
Estimates_pairwise <- filldata_maregression(model = mod0_G7_CheCran,
empty_dataset = Estimates_pairwise,
generation = 7,
pairwise = "Cherry_Cranberry")
#######
####### Cranberry Strawberry
#######
mod0_G7_CranStraw <- lmodel2::lmodel2(logchange_allop ~ logchange_symp,
range.y = "interval",range.x = "interval",
data = TEMP_dataG7_CranStraw, nperm=1000)
mod0_G7_CranStraw
##
## Model II regression
##
## Call: lmodel2::lmodel2(formula = logchange_allop ~ logchange_symp, data
## = TEMP_dataG7_CranStraw, range.y = "interval", range.x = "interval",
## nperm = 1000)
##
## n = 10 r = 0.2624681 r-square = 0.0688895
## Parametric P-values: 2-tailed = 0.4637958 1-tailed = 0.2318979
## Angle between the two OLS regression lines = 58.18591 degrees
##
## Permutation tests of OLS, MA, RMA slopes: 1-tailed, tail corresponding to sign
## A permutation test of r is equivalent to a permutation test of the OLS slope
## P-perm for SMA = NA because the SMA slope cannot be tested
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 0.07778912 0.4093331 22.26091 0.2337662
## 2 MA -0.62085655 3.7645194 75.12361 0.2337662
## 3 SMA -0.16171969 1.5595538 57.33164 NA
## 4 RMA -0.23805813 1.9261626 62.56315 0.2337662
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS -0.2590617 0.414639991 -0.8175863 1.636253
## 2 MA 0.4815458 0.056590854 0.5111360 -1.529674
## 3 SMA -0.5056486 0.005310832 0.7574039 3.211243
## 4 RMA NA NA NA NA
##
## Eigenvalues: 0.09039719 0.03170788
##
## H statistic used for computing C.I. of MA: 0.553139
#Value:
mod0_G7_CranStraw$confidence.intervals[2,]
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 2 MA 0.4815458 0.05659085 0.511136 -1.529674
mod0_G7_CranStraw$regression.results[2,]
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 2 MA -0.6208566 3.764519 75.12361 0.2337662
#Save slope, intercept and ci
Estimates_pairwise <- filldata_maregression(model = mod0_G7_CranStraw,
empty_dataset = Estimates_pairwise,
generation = 7,
pairwise = "Cranberry_Strawberry")
#######
####### Strawberry Cherry
#######
mod0_G7_StrawChe <- lmodel2::lmodel2(logchange_allop ~ logchange_symp,
range.y = "interval",range.x = "interval",
data = TEMP_dataG7_StrawChe, nperm=1000)
mod0_G7_StrawChe
##
## Model II regression
##
## Call: lmodel2::lmodel2(formula = logchange_allop ~ logchange_symp, data
## = TEMP_dataG7_StrawChe, range.y = "interval", range.x = "interval",
## nperm = 1000)
##
## n = 9 r = 0.5256197 r-square = 0.2762761
## Parametric P-values: 2-tailed = 0.1461308 1-tailed = 0.07306542
## Angle between the two OLS regression lines = 33.24182 degrees
##
## Permutation tests of OLS, MA, RMA slopes: 1-tailed, tail corresponding to sign
## A permutation test of r is equivalent to a permutation test of the OLS slope
## P-perm for SMA = NA because the SMA slope cannot be tested
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 0.3229824 0.3831600 20.96484 0.08091908
## 2 MA 0.3072319 0.5606481 29.27709 0.08091908
## 3 SMA 0.2922950 0.7289682 36.09086 NA
## 4 RMA 0.2854344 0.8062784 38.87848 0.08091908
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 0.1430614 0.5029034 -0.17109307 0.9374131
## 2 MA 0.1440322 0.3707596 -0.15522634 2.3996977
## 3 SMA 0.2265349 0.3249052 0.36149308 1.4699993
## 4 RMA 0.9000034 0.3508269 0.06938925 -6.1191182
##
## Eigenvalues: 0.1332536 0.03472534
##
## H statistic used for computing C.I. of MA: 0.3807415
#Value:
mod0_G7_StrawChe$regression.results[2,]
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 2 MA 0.3072319 0.5606481 29.27709 0.08091908
mod0_G7_StrawChe$confidence.intervals[2,]
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 2 MA 0.1440322 0.3707596 -0.1552263 2.399698
#Save slope, intercept and ci
Estimates_pairwise <- filldata_maregression(model = mod0_G7_StrawChe,
empty_dataset = Estimates_pairwise,
generation = 7,
pairwise = "Strawberry_Cherry")
#########################
######################### G29
#########################
#######
####### Cherry Cranberry
#######
mod0_G29_CheCran <- lmodel2::lmodel2(logchange_allop ~ logchange_symp,
range.y = "interval",range.x = "interval",
data = TEMP_dataG29_CheCran, nperm=1000)
mod0_G29_CheCran
##
## Model II regression
##
## Call: lmodel2::lmodel2(formula = logchange_allop ~ logchange_symp, data
## = TEMP_dataG29_CheCran, range.y = "interval", range.x = "interval",
## nperm = 1000)
##
## n = 8 r = -0.5675571 r-square = 0.322121
## Parametric P-values: 2-tailed = 0.142274 1-tailed = 0.071137
## Angle between the two OLS regression lines = 30.32302 degrees
##
## Permutation tests of OLS, MA, RMA slopes: 1-tailed, tail corresponding to sign
## A permutation test of r is equivalent to a permutation test of the OLS slope
## P-perm for SMA = NA because the SMA slope cannot be tested
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 0.1313351 -0.6964883 -34.85676 0.07492507
## 2 MA 0.2684057 -1.4271279 -54.98076 0.07492507
## 3 SMA 0.2308927 -1.2271686 -50.82396 NA
## 4 RMA 0.2219698 -1.1796064 -49.71071 0.07492507
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS -0.20236731 0.4650375 -1.705795 0.3128181
## 2 MA 0.04073939 -1.3503555 7.201497 -0.2135780
## 3 SMA 0.10940754 0.4881066 -2.598219 -0.5796059
## 4 RMA -0.01543750 9.6736998 -51.560993 0.0858666
##
## Eigenvalues: 0.1689027 0.04336671
##
## H statistic used for computing C.I. of MA: 0.4638122
#Value:
mod0_G29_CheCran$regression.results[2,]
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 2 MA 0.2684057 -1.427128 -54.98076 0.07492507
mod0_G29_CheCran$confidence.intervals[2,]
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 2 MA 0.04073939 -1.350356 7.201497 -0.213578
Estimates_pairwise <- filldata_maregression(model = mod0_G29_CheCran,
empty_dataset = Estimates_pairwise,
generation = 29,
pairwise = "Cherry_Cranberry")
#######
####### Cranberry Strawberry
#######
mod0_G29_CranStraw <- lmodel2::lmodel2(logchange_allop ~ logchange_symp,
range.y = "interval",range.x = "interval",
data = TEMP_dataG29_CranStraw, nperm=1000)
mod0_G29_CranStraw
##
## Model II regression
##
## Call: lmodel2::lmodel2(formula = logchange_allop ~ logchange_symp, data
## = TEMP_dataG29_CranStraw, range.y = "interval", range.x = "interval",
## nperm = 1000)
##
## n = 8 r = 0.5934942 r-square = 0.3522353
## Parametric P-values: 2-tailed = 0.1208975 1-tailed = 0.06044873
## Angle between the two OLS regression lines = 27.62815 degrees
##
## Permutation tests of OLS, MA, RMA slopes: 1-tailed, tail corresponding to sign
## A permutation test of r is equivalent to a permutation test of the OLS slope
## P-perm for SMA = NA because the SMA slope cannot be tested
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS -0.3955687 0.7939019 38.44613 0.05794206
## 2 MA -0.5829369 1.6139086 58.21707 0.05794206
## 3 SMA -0.5198185 1.3376743 53.21945 NA
## 4 RMA -0.4703885 1.1213465 48.27390 0.05794206
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS -0.8005274 0.009389961 -0.2815765 1.869380
## 2 MA 2.1350787 -0.301322822 0.3814396 -10.281344
## 3 SMA -0.8520979 -0.360613203 0.6409208 2.791878
## 4 RMA -1.3469425 -0.176630702 -0.1642689 4.957539
##
## Eigenvalues: 0.2335271 0.05201112
##
## H statistic used for computing C.I. of MA: 0.3678656
#Value:
mod0_G29_CranStraw$confidence.intervals[2,]
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 2 MA 2.135079 -0.3013228 0.3814396 -10.28134
mod0_G29_CranStraw$regression.results[2,]
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 2 MA -0.5829369 1.613909 58.21707 0.05794206
#Save slope, intercept and ci
Estimates_pairwise <- filldata_maregression(model = mod0_G29_CranStraw,
empty_dataset = Estimates_pairwise,
generation = 29,
pairwise = "Cranberry_Strawberry")
#######
####### Strawberry Cherry
#######
mod0_G29_StrawChe <- lmodel2::lmodel2(logchange_allop ~ logchange_symp,
range.y = "interval",range.x = "interval",
data = TEMP_dataG29_StrawChe, nperm=1000)
mod0_G29_StrawChe
##
## Model II regression
##
## Call: lmodel2::lmodel2(formula = logchange_allop ~ logchange_symp, data
## = TEMP_dataG29_StrawChe, range.y = "interval", range.x = "interval",
## nperm = 1000)
##
## n = 6 r = 0.6760006 r-square = 0.4569768
## Parametric P-values: 2-tailed = 0.1404574 1-tailed = 0.07022871
## Angle between the two OLS regression lines = 18.01702 degrees
##
## Permutation tests of OLS, MA, RMA slopes: 1-tailed, tail corresponding to sign
## A permutation test of r is equivalent to a permutation test of the OLS slope
## P-perm for SMA = NA because the SMA slope cannot be tested
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS -0.3926339 1.324565 52.94847 0.06493506
## 2 MA -0.7647082 2.537640 68.49224 0.06493506
## 3 SMA -0.5873546 1.959413 62.96220 NA
## 4 RMA -0.5806572 1.937578 62.70136 0.06393606
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS -1.070322 0.28505396 -0.6798814 3.329010
## 2 MA 1.833145 -0.22100365 0.7649980 -5.932149
## 3 SMA -1.460919 -0.23131211 0.7986067 4.807499
## 4 RMA 11.233172 0.06876396 -0.1797323 -36.579089
##
## Eigenvalues: 0.1058706 0.01160433
##
## H statistic used for computing C.I. of MA: 0.2664412
#Value:
mod0_G29_StrawChe$regression.results[2,]
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 2 MA -0.7647082 2.53764 68.49224 0.06493506
mod0_G29_StrawChe$confidence.intervals[2,]
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 2 MA 1.833145 -0.2210037 0.764998 -5.932149
#Save slope, intercept and ci
Estimates_pairwise <- filldata_maregression(model = mod0_G29_StrawChe,
empty_dataset = Estimates_pairwise,
generation = 29,
pairwise = "Strawberry_Cherry")
Estimates_pairwise
## Variables Generation Pairwise Estimates
## 1 slope 7 Cherry_Cranberry 2.86626806
## 2 slope_CI_inf 7 Cherry_Cranberry 0.96037934
## 3 slope_CI_sup 7 Cherry_Cranberry -7.40440288
## 4 intercept 7 Cherry_Cranberry -0.03441332
## 5 intercept_CI_inf 7 Cherry_Cranberry 0.23775279
## 6 intercept_CI_sup 7 Cherry_Cranberry 0.01609149
## 7 slope 29 Cherry_Cranberry -1.42712789
## 8 slope_CI_inf 29 Cherry_Cranberry 7.20149738
## 9 slope_CI_sup 29 Cherry_Cranberry -0.21357803
## 10 intercept 29 Cherry_Cranberry 0.26840573
## 11 intercept_CI_inf 29 Cherry_Cranberry 0.04073939
## 12 intercept_CI_sup 29 Cherry_Cranberry -1.35035553
## 13 slope 7 Cranberry_Strawberry 3.76451938
## 14 slope_CI_inf 7 Cranberry_Strawberry 0.51113600
## 15 slope_CI_sup 7 Cranberry_Strawberry -1.52967376
## 16 intercept 7 Cranberry_Strawberry -0.62085655
## 17 intercept_CI_inf 7 Cranberry_Strawberry 0.48154577
## 18 intercept_CI_sup 7 Cranberry_Strawberry 0.05659085
## 19 slope 29 Cranberry_Strawberry 1.61390856
## 20 slope_CI_inf 29 Cranberry_Strawberry 0.38143958
## 21 slope_CI_sup 29 Cranberry_Strawberry -10.28134362
## 22 intercept 29 Cranberry_Strawberry -0.58293685
## 23 intercept_CI_inf 29 Cranberry_Strawberry 2.13507874
## 24 intercept_CI_sup 29 Cranberry_Strawberry -0.30132282
## 25 slope 7 Strawberry_Cherry 0.56064812
## 26 slope_CI_inf 7 Strawberry_Cherry -0.15522634
## 27 slope_CI_sup 7 Strawberry_Cherry 2.39969770
## 28 intercept 7 Strawberry_Cherry 0.30723191
## 29 intercept_CI_inf 7 Strawberry_Cherry 0.14403217
## 30 intercept_CI_sup 7 Strawberry_Cherry 0.37075958
## 31 slope 29 Strawberry_Cherry 2.53763976
## 32 slope_CI_inf 29 Strawberry_Cherry 0.76499799
## 33 slope_CI_sup 29 Strawberry_Cherry -5.93214871
## 34 intercept 29 Strawberry_Cherry -0.76470822
## 35 intercept_CI_inf 29 Strawberry_Cherry 1.83314474
## 36 intercept_CI_sup 29 Strawberry_Cherry -0.22100365
Estimates_pairwise[Estimates_pairwise$Variables == "slope",]
## Variables Generation Pairwise Estimates
## 1 slope 7 Cherry_Cranberry 2.8662681
## 7 slope 29 Cherry_Cranberry -1.4271279
## 13 slope 7 Cranberry_Strawberry 3.7645194
## 19 slope 29 Cranberry_Strawberry 1.6139086
## 25 slope 7 Strawberry_Cherry 0.5606481
## 31 slope 29 Strawberry_Cherry 2.5376398
Analysis
# ##### Create empty dataframe with slopes and confidence interval for all pairwise
Estimates_pairwise <- data.frame(Variables = rep(rep(c("slope", "slope_CI_inf", "slope_CI_sup",
"intercept"),2),3),
Generation = rep(c(rep(7, 4), rep(29, 4)),3),
Pairwise = rep(c("Cherry_Cranberry",
"Cranberry_Strawberry",
"Strawberry_Cherry"), each=8),
Estimates = NA)
## Nbr sample
nb_sim = 50000
#########################
######################### G7
#########################
#######
####### Cherry Cranberry
#######
# MA axis regression with bootstrap to estimate slope, intercept and
sim <- data.frame(t(sapply(1:nb_sim, bootstrap_lmodel2, data_pairwise = TEMP_dataG7_CheCran)))
# Fill out data with quantile of simulations
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="intercept"] <- quantile(sim$intercept,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="slope"] <- quantile(sim$slope,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="slope_CI_inf"] <- quantile(sim$slope,probs = 0.025, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="slope_CI_sup"] <- quantile(sim$slope,probs = 0.975, na.rm = TRUE)
#######
####### Cranberry Strawberry
#######
# MA axis regression with bootstrap to estimate slope, intercept and
sim <- data.frame(t(sapply(1:nb_sim, bootstrap_lmodel2, data_pairwise = TEMP_dataG7_CranStraw)))
# Fill out data with quantile of simulations
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="intercept"] <- quantile(sim$intercept,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="slope"] <- quantile(sim$slope,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="slope_CI_inf"] <- quantile(sim$slope,probs = 0.025, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="slope_CI_sup"] <- quantile(sim$slope,probs = 0.975, na.rm = TRUE)
#######
####### Strawberry Cherry
#######
# MA axis regression with bootstrap to estimate slope, intercept and
sim <- data.frame(t(sapply(1:nb_sim, bootstrap_lmodel2, data_pairwise = TEMP_dataG7_StrawChe)))
# Fill out data with quantile of simulations
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="intercept"] <- quantile(sim$intercept,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="slope"] <- quantile(sim$slope,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="slope_CI_inf"] <- quantile(sim$slope,probs = 0.025, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==7&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="slope_CI_sup"] <- quantile(sim$slope,probs = 0.975, na.rm = TRUE)
#########################
######################### G29
#########################
#######
####### Cherry Cranberry
#######
# MA axis regression with bootstrap to estimate slope, intercept and
sim <- data.frame(t(sapply(1:nb_sim, bootstrap_lmodel2, data_pairwise = TEMP_dataG29_CheCran)))
# Fill out data with quantile of simulations
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="intercept"] <- quantile(sim$intercept,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="slope"] <- quantile(sim$slope,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="slope_CI_inf"] <- quantile(sim$slope,probs = 0.025, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cherry_Cranberry"&
Estimates_pairwise$Variables=="slope_CI_sup"] <- quantile(sim$slope,probs = 0.975, na.rm = TRUE)
#######
####### Cranberry Strawberry
#######
# MA axis regression with bootstrap to estimate slope, intercept and
sim <- data.frame(t(sapply(1:nb_sim, bootstrap_lmodel2, data_pairwise = TEMP_dataG29_CranStraw)))
# Fill out data with quantile of simulations
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="intercept"] <- quantile(sim$intercept,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="slope"] <- quantile(sim$slope,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="slope_CI_inf"] <- quantile(sim$slope,probs = 0.025, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Cranberry_Strawberry"&
Estimates_pairwise$Variables=="slope_CI_sup"] <- quantile(sim$slope,probs = 0.975, na.rm = TRUE)
#######
####### Strawberry Cherry
#######
rm(sim)
# MA axis regression with bootstrap to estimate slope, intercept and
sim <- data.frame(t(sapply(1:nb_sim, bootstrap_lmodel2, data_pairwise = TEMP_dataG29_StrawChe)))
# Fill out data with quantile of simulations
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="intercept"] <- quantile(sim$intercept,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="slope"] <- quantile(sim$slope,probs = 0.5, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="slope_CI_inf"] <- quantile(sim$slope,probs = 0.025, na.rm = TRUE)
Estimates_pairwise$Estimates[Estimates_pairwise$Generation==29&
Estimates_pairwise$Pairwise=="Strawberry_Cherry"&
Estimates_pairwise$Variables=="slope_CI_sup"] <- quantile(sim$slope,probs = 0.975, na.rm = TRUE)
Estimates_pairwise
## Variables Generation Pairwise Estimates
## 1 slope 7 Cherry_Cranberry 3.53097589
## 2 slope_CI_inf 7 Cherry_Cranberry -12.92050856
## 3 slope_CI_sup 7 Cherry_Cranberry 27.64250563
## 4 intercept 7 Cherry_Cranberry -0.60944265
## 5 slope 29 Cherry_Cranberry -1.63420934
## 6 slope_CI_inf 29 Cherry_Cranberry -3.85537185
## 7 slope_CI_sup 29 Cherry_Cranberry 0.12873229
## 8 intercept 29 Cherry_Cranberry 0.24873663
## 9 slope 7 Cranberry_Strawberry 1.27496614
## 10 slope_CI_inf 7 Cranberry_Strawberry 0.41028395
## 11 slope_CI_sup 7 Cranberry_Strawberry 7.14067158
## 12 intercept 7 Cranberry_Strawberry -0.06059237
## 13 slope 29 Cranberry_Strawberry 1.50471601
## 14 slope_CI_inf 29 Cranberry_Strawberry 0.14547472
## 15 slope_CI_sup 29 Cranberry_Strawberry 3.80483075
## 16 intercept 29 Cranberry_Strawberry -0.49094357
## 17 slope 7 Strawberry_Cherry 1.15597786
## 18 slope_CI_inf 7 Strawberry_Cherry 0.12169649
## 19 slope_CI_sup 7 Strawberry_Cherry 3.91063251
## 20 intercept 7 Strawberry_Cherry 0.06456127
## 21 slope 29 Strawberry_Cherry 2.45170190
## 22 slope_CI_inf 29 Strawberry_Cherry 0.45700174
## 23 slope_CI_sup 29 Strawberry_Cherry 4.82393848
## 24 intercept 29 Strawberry_Cherry -0.77826044
Estimates_pairwise[Estimates_pairwise$Variables == "slope_CI_inf"|
Estimates_pairwise$Variables == "slope_CI_sup",]
## Variables Generation Pairwise Estimates
## 2 slope_CI_inf 7 Cherry_Cranberry -12.9205086
## 3 slope_CI_sup 7 Cherry_Cranberry 27.6425056
## 6 slope_CI_inf 29 Cherry_Cranberry -3.8553718
## 7 slope_CI_sup 29 Cherry_Cranberry 0.1287323
## 10 slope_CI_inf 7 Cranberry_Strawberry 0.4102839
## 11 slope_CI_sup 7 Cranberry_Strawberry 7.1406716
## 14 slope_CI_inf 29 Cranberry_Strawberry 0.1454747
## 15 slope_CI_sup 29 Cranberry_Strawberry 3.8048307
## 18 slope_CI_inf 7 Strawberry_Cherry 0.1216965
## 19 slope_CI_sup 7 Strawberry_Cherry 3.9106325
## 22 slope_CI_inf 29 Strawberry_Cherry 0.4570017
## 23 slope_CI_sup 29 Strawberry_Cherry 4.8239385
##########
########## ##########
########## ##########
########## ##########
########## ##########
########## ##########
##########
##########
##########
########## NEW FUNCTION
temp_polygon_limits <- function(empty_dataset = Estimates_pairwise, generation = 7, pairwise = "Cherry_Cranberry") {
# Limits for plot (arbitrary)
ymin=-50
ymax=50
empty_dataset<-empty_dataset[empty_dataset$Generation==generation&
empty_dataset$Pairwise==pairwise,]
# Create empty dataframe with slopes and confidence interval for all pairwise
if (sign(empty_dataset$Estimates[empty_dataset$Variables=="slope_CI_inf"])!=
sign(empty_dataset$Estimates[empty_dataset$Variables=="slope_CI_sup"])) {
# Polygon limits
intercept <- empty_dataset$Estimates[empty_dataset$Variables == "intercept"]
inf_min<-(empty_dataset$Estimates[empty_dataset$Variables=="slope_CI_inf"]*ymin)+intercept
inf_max<-(empty_dataset$Estimates[empty_dataset$Variables=="slope_CI_inf"]*ymax)+intercept
sup_min <- (ymin-intercept)/empty_dataset$Estimates[empty_dataset$Variables == "slope_CI_sup"]
sup_max <- (ymax-intercept)/empty_dataset$Estimates[empty_dataset$Variables == "slope_CI_sup"]
# Dataframe with coordiantes
polygon_data <- data.frame (xvalue = c(ymin, sup_min,
sup_max,ymax),
yvalue = c(inf_min,ymin,
ymax, inf_max))
} else {
# Polygon limits
intercept <- empty_dataset$Estimates[empty_dataset$Variables == "intercept"]
inf_min <- (ymin-intercept)/empty_dataset$Estimates[empty_dataset$Variables == "slope_CI_inf"]
inf_max <- (ymax-intercept)/empty_dataset$Estimates[empty_dataset$Variables == "slope_CI_inf"]
sup_min <- (ymin-intercept)/empty_dataset$Estimates[empty_dataset$Variables == "slope_CI_sup"]
sup_max <- (ymax-intercept)/empty_dataset$Estimates[empty_dataset$Variables == "slope_CI_sup"]
# Dataframe with coordiantes
polygon_data <- data.frame (xvalue = c(inf_min,sup_min,
sup_max,inf_max),
yvalue = c(ymin, ymin,
ymax, ymax))
}
return(polygon_data)
}
Plot
ymin =-50
ymax =50
##################################################
################## G7
# Calculate polygon limits and plot limits
#polygon_CheCranG7<-filldata_predict_maregression(model = mod0_G7_CheCran)
polygon_CheCranG7<-temp_polygon_limits(Estimates_pairwise, 7, "Cherry_Cranberry")
ymin_CheCranG7=min(min(TEMP_dataG7_CheCran$logchange_allop-1.96*TEMP_dataG7_CheCran$sd_allop, na.rm= TRUE),
min(TEMP_dataG7_CheCran$logchange_symp-1.96*TEMP_dataG7_CheCran$sd_symp, na.rm= TRUE))
ymax_CheCranG7=max(max(TEMP_dataG7_CheCran$logchange_allop + 1.96*TEMP_dataG7_CheCran$sd_allop, na.rm= TRUE),
max(TEMP_dataG7_CheCran$logchange_symp + 1.96*TEMP_dataG7_CheCran$sd_symp, na.rm= TRUE))
# Plot
CheCran_G7 <- ggplot() +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "slope_CI_inf"],
colour = "grey", linetype = "dotted") +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "slope_CI_sup"],
colour = "grey", linetype = "dotted") +
geom_errorbar(data = TEMP_dataG7_CheCran,
aes(x = logchange_symp, ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp, linetype = Line_type),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(data = TEMP_dataG7_CheCran,
aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp, linetype = Line_type),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(data = TEMP_dataG7_CheCran,
aes(x = logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size =3, fill = "white", stroke =1.2) +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Cherry vs. Cranberry") +
theme_LO_sober +
labs(shape = "Test fruit", color = "Evolution fruit") +
geom_polygon(data = polygon_CheCranG7, aes(x = xvalue, y = yvalue), fill = "grey", alpha = 0.2) +
scale_shape_manual(values = c(21, 22)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424")) +
coord_cartesian(ylim = c(ymin_CheCranG7, ymax_CheCranG7),
xlim = c(ymin_CheCranG7, ymax_CheCranG7))
CheCran_G7
# Calculate polygon limits and plot limits
#polygon_CranStrawG7<-filldata_predict_maregression(model = mod0_G7_CranStraw)
polygon_CranStrawG7<-temp_polygon_limits(Estimates_pairwise, 7, "Cranberry_Strawberry")
ymin_CranStrawG7=min(min(TEMP_dataG7_CranStraw$logchange_allop-1.96*TEMP_dataG7_CranStraw$sd_allop, na.rm= TRUE),
min(TEMP_dataG7_CranStraw$logchange_symp-1.96*TEMP_dataG7_CranStraw$sd_symp, na.rm= TRUE))
ymax_CranStrawG7=max(max(TEMP_dataG7_CranStraw$logchange_allop + 1.96*TEMP_dataG7_CranStraw$sd_allop, na.rm= TRUE),
max(TEMP_dataG7_CranStraw$logchange_symp + 1.96*TEMP_dataG7_CranStraw$sd_symp, na.rm= TRUE))
CranStraw_G7 <- ggplot() +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "slope_CI_inf"],
colour = "grey", linetype = "dotted") +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "slope_CI_sup"],
colour = "grey", linetype = "dotted") +
geom_errorbar(data = TEMP_dataG7_CranStraw,
aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp, linetype = Line_type),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(data = TEMP_dataG7_CranStraw,
aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp, linetype = Line_type),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(data = TEMP_dataG7_CranStraw,
aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size =3, fill = "white", stroke =1.2) +
geom_polygon(data =polygon_CranStrawG7, aes(x = xvalue, y = yvalue), fill = "grey", alpha = 0.2) +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Cranberry vs. Strawberry") +
coord_cartesian(ylim = c(ymin_CranStrawG7, ymax_CranStrawG7),
xlim = c(ymin_CranStrawG7, ymax_CranStrawG7)) +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(22, 24)) +
scale_color_manual(values = c("#FDB424", "#3FAA96")) +
theme_LO_sober
CranStraw_G7
#Calculate polygon limits and plot limits
#polygon_StrawCheG7<-filldata_predict_maregression(model = mod0_G7_StrawChe)
polygon_StrawCheG7<-temp_polygon_limits(Estimates_pairwise, 7, "Strawberry_Cherry")
ymin_StrawCheG7=min(min(TEMP_dataG7_StrawChe$logchange_allop-1.96*TEMP_dataG7_StrawChe$sd_allop, na.rm= TRUE),
min(TEMP_dataG7_StrawChe$logchange_symp-1.96*TEMP_dataG7_StrawChe$sd_symp, na.rm= TRUE))
ymax_StrawCheG7=max(max(TEMP_dataG7_StrawChe$logchange_allop + 1.96*TEMP_dataG7_StrawChe$sd_allop, na.rm= TRUE),
max(TEMP_dataG7_StrawChe$logchange_symp + 1.96*TEMP_dataG7_StrawChe$sd_symp, na.rm= TRUE))
StrawChe_G7 <- ggplot() +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "slope_CI_inf"],
colour = "grey", linetype = "dotted") +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation == 7 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "slope_CI_sup"],
colour = "grey", linetype = "dotted") +
geom_errorbar(data = TEMP_dataG7_StrawChe,
aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop), ymax = logchange_allop + (1.96*sd_allop),
color = Symp, linetype = Line_type),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(data = TEMP_dataG7_StrawChe,
aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp, linetype = Line_type),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(data = TEMP_dataG7_StrawChe,
aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size =3, fill = "white", stroke =1.2) +
geom_polygon(data =polygon_StrawCheG7, aes(x = xvalue, y = yvalue), fill = "grey", alpha = 0.2) +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Strawberry vs. Cherry") +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(21, 24)) +
scale_color_manual(values = c("#BC3C6D", "#3FAA96")) +
coord_cartesian(ylim = c(ymin_StrawCheG7, ymax_StrawCheG7),
xlim = c(ymin_StrawCheG7, ymax_StrawCheG7)) +
theme_LO_sober
StrawChe_G7
#####################################################################################
################################## G29 ##################################
#####################################################################################
TEMP_dataG29_CheCran
## Line Treatment Fruit_s Generation logchange sd_logchange SA Symp
## 14 CEA Cranberry Cherry 29 -0.21774071 0.13281291 0 Cherry
## 17 CEB Cranberry Cherry 29 -0.59200204 0.09634940 0 Cherry
## 21 CEC Cranberry Cherry 29 -0.20670829 0.12591289 0 Cherry
## 37 CRA Cherry Cranberry 29 0.32537794 0.10025669 0 Cranberry
## 42 CRB Cherry Cranberry 29 0.16929862 0.12699030 0 Cranberry
## 45 CRC Cherry Cranberry 29 0.08474123 0.12334598 0 Cranberry
## 47 CRD Cherry Cranberry 29 0.54736475 0.09718018 0 Cranberry
## 49 CRE Cherry Cranberry 29 -0.10496044 0.11556266 0 Cranberry
## Allop Line_type logchange_allop sd_allop logchange_symp sd_symp
## 14 Cranberry dashed -0.21774071 0.13281291 0.14767000 0.1574284
## 17 Cranberry dashed -0.59200204 0.09634940 0.25038029 0.1009864
## 21 Cranberry dashed -0.20670829 0.12591289 0.35854062 0.1169300
## 37 Cherry dashed 0.32537794 0.10025669 -0.14885350 0.1193552
## 42 Cherry dashed 0.16929862 0.12699030 0.29719689 0.1531197
## 45 Cherry dashed 0.08474123 0.12334598 0.02703717 0.1156381
## 47 Cherry dashed 0.54736475 0.09718018 -0.15627744 0.1156695
## 49 Cherry dashed -0.10496044 0.11556266 0.72513486 0.1275430
TEMP_dataG29_CranStraw
## Line Treatment Fruit_s Generation logchange sd_logchange SA Symp
## 39 CRA Strawberry Cranberry 29 -0.29731422 0.1098141 0 Cranberry
## 40 CRB Strawberry Cranberry 29 -0.24539416 0.1387043 0 Cranberry
## 43 CRC Strawberry Cranberry 29 -0.28950778 0.2012393 0 Cranberry
## 48 CRD Strawberry Cranberry 29 -1.02555272 0.1902082 0 Cranberry
## 51 CRE Strawberry Cranberry 29 -0.16810249 0.1398057 0 Cranberry
## 69 FRA Cranberry Strawberry 29 -0.25158762 0.1712755 0 Strawberry
## 71 FRB Cranberry Strawberry 29 0.04303751 0.1441119 0 Strawberry
## 74 FRC Cranberry Strawberry 29 0.52109773 0.1254820 0 Strawberry
## Allop Line_type logchange_allop sd_allop logchange_symp sd_symp
## 39 Strawberry dashed -0.29731422 0.1098141 -0.14885350 0.1193552
## 40 Strawberry dashed -0.24539416 0.1387043 0.29719689 0.1531197
## 43 Strawberry dashed -0.28950778 0.2012393 0.02703717 0.1156381
## 48 Strawberry dashed -1.02555272 0.1902082 -0.15627744 0.1156695
## 51 Strawberry dashed -0.16810249 0.1398057 0.72513486 0.1275430
## 69 Cranberry dashed -0.25158762 0.1712755 0.15709032 0.1411560
## 71 Cranberry dashed 0.04303751 0.1441119 0.56023100 0.1284283
## 74 Cranberry dashed 0.52109773 0.1254820 0.36640738 0.1201170
TEMP_dataG29_StrawChe
## Line Treatment Fruit_s Generation logchange sd_logchange SA Symp
## 13 CEA Strawberry Cherry 29 0.01971359 0.1475176 0 Cherry
## 16 CEB Strawberry Cherry 29 -0.15850680 0.0983504 0 Cherry
## 19 CEC Strawberry Cherry 29 0.21131464 0.1237821 0 Cherry
## 68 FRA Cherry Strawberry 29 -0.50599095 0.1378652 0 Strawberry
## 72 FRB Cherry Strawberry 29 0.19377147 0.1094819 0 Strawberry
## 75 FRC Cherry Strawberry 29 0.32151693 0.1140574 0 Strawberry
## Allop Line_type logchange_allop sd_allop logchange_symp sd_symp
## 13 Strawberry dashed 0.01971359 0.1475176 0.1476700 0.1574284
## 16 Strawberry dashed -0.15850680 0.0983504 0.2503803 0.1009864
## 19 Strawberry dashed 0.21131464 0.1237821 0.3585406 0.1169300
## 68 Cherry dashed -0.50599095 0.1378652 0.1570903 0.1411560
## 72 Cherry dashed 0.19377147 0.1094819 0.5602310 0.1284283
## 75 Cherry dashed 0.32151693 0.1140574 0.3664074 0.1201170
#Calculate polygon limits and plot limits
polygon_CheCranG29<-filldata_predict_maregression(model = mod0_G29_CheCran)
polygon_CheCranG29<-temp_polygon_limits(Estimates_pairwise, 29, "Cherry_Cranberry")
ymin_CheCranG29=min(min(TEMP_dataG29_CheCran$logchange_allop-1.96*TEMP_dataG29_CheCran$sd_allop, na.rm= TRUE),
min(TEMP_dataG29_CheCran$logchange_symp-1.96*TEMP_dataG29_CheCran$sd_symp, na.rm= TRUE))
ymax_CheCranG29=max(max(TEMP_dataG29_CheCran$logchange_allop + 1.96*TEMP_dataG29_CheCran$sd_allop, na.rm= TRUE),
max(TEMP_dataG29_CheCran$logchange_symp + 1.96*TEMP_dataG29_CheCran$sd_symp, na.rm= TRUE))
CheCran_G29 <- ggplot() +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "slope_CI_inf"],
colour = "grey", linetype = "dotted") +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cherry_Cranberry" &
Estimates_pairwise$Variables == "slope_CI_sup"],
colour = "grey", linetype = "dotted") +
geom_errorbar(data = TEMP_dataG29_CheCran,
aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(data = TEMP_dataG29_CheCran,
aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(data = TEMP_dataG29_CheCran,
aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size =3, fill = "white", stroke =1.2) +
geom_polygon(data =polygon_CheCranG29, aes(x = xvalue, y = yvalue), fill = "grey", alpha = 0.2) +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Cherry vs. Cranberry") +
labs(shape = "Test fruit", color = "Evolution fruit") +
scale_shape_manual(values = c(16, 15)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424")) +
coord_cartesian(ylim = c(ymin_CheCranG29, ymax_CheCranG29),
xlim = c(ymin_CheCranG29, ymax_CheCranG29)) +
theme_LO_sober
CheCran_G29
#Calculate polygon limits and plot limits
polygon_CranStrawG29<-filldata_predict_maregression(model = mod0_G29_CranStraw)
polygon_CranStrawG29<-temp_polygon_limits(Estimates_pairwise, 29, "Cranberry_Strawberry")
ymin_CranStrawG29=min(min(TEMP_dataG29_CranStraw$logchange_allop-1.96*TEMP_dataG29_CranStraw$sd_allop, na.rm= TRUE),
min(TEMP_dataG29_CranStraw$logchange_symp-1.96*TEMP_dataG29_CranStraw$sd_symp, na.rm= TRUE))
ymax_CranStrawG29=max(max(TEMP_dataG29_CranStraw$logchange_allop + 1.96*TEMP_dataG29_CranStraw$sd_allop, na.rm= TRUE),
max(TEMP_dataG29_CranStraw$logchange_symp + 1.96*TEMP_dataG29_CranStraw$sd_symp, na.rm= TRUE))
#Plot
CranStraw_G29 <- ggplot() +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "slope_CI_inf"],
colour = "grey", linetype = "dotted") +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Cranberry_Strawberry" &
Estimates_pairwise$Variables == "slope_CI_sup"],
colour = "grey", linetype = "dotted") +
geom_errorbar(data = TEMP_dataG29_CranStraw,
aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(data = TEMP_dataG29_CranStraw,
aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(data = TEMP_dataG29_CranStraw,
aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size =3, fill = "white", stroke =1.2) +
geom_polygon(data =polygon_CranStrawG29, aes(x = xvalue, y = yvalue), fill = "grey", alpha = 0.2) +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Cranberry vs. Strawberry") +
coord_cartesian(ylim = c(ymin_CranStrawG29, ymax_CranStrawG29),
xlim = c(ymin_CranStrawG29, ymax_CranStrawG29)) +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(15, 17)) +
scale_color_manual(values = c("#FDB424", "#3FAA96")) +
theme_LO_sober
CranStraw_G29
#Calculate polygon limits and plot limits
polygon_StrawCheG29<-filldata_predict_maregression(model = mod0_G29_StrawChe)
polygon_StrawCheG29<-temp_polygon_limits(Estimates_pairwise, 29, "Strawberry_Cherry")
ymin_StrawCheG29=min(min(TEMP_dataG29_StrawChe$logchange_allop-1.96*TEMP_dataG29_StrawChe$sd_allop, na.rm= TRUE),
min(TEMP_dataG29_StrawChe$logchange_symp-1.96*TEMP_dataG29_StrawChe$sd_symp, na.rm= TRUE))
ymax_StrawCheG29=max(max(TEMP_dataG29_StrawChe$logchange_allop + 1.96*TEMP_dataG29_StrawChe$sd_allop, na.rm= TRUE),
max(TEMP_dataG29_StrawChe$logchange_symp + 1.96*TEMP_dataG29_StrawChe$sd_symp, na.rm= TRUE))
StrawChe_G29 <- ggplot() +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "slope"],
colour = "black", size = 0.75) +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "slope_CI_inf"],
colour = "grey", linetype = "dotted") +
geom_abline(intercept = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "intercept"],
slope = Estimates_pairwise$Estimates[Estimates_pairwise$Generation ==29 &
Estimates_pairwise$Pairwise == "Strawberry_Cherry" &
Estimates_pairwise$Variables == "slope_CI_sup"],
colour = "grey", linetype = "dotted") +
geom_errorbar(data = TEMP_dataG29_StrawChe,
aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp),
width= 0.02, size = 0.2, alpha =1) +
geom_errorbarh(data = TEMP_dataG29_StrawChe,
aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp),
height = 0.02, size = 0.2, alpha =1) +
geom_point(data = TEMP_dataG29_StrawChe,
aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size =3, fill = "white", stroke =1.2) +
geom_polygon(data =polygon_StrawCheG29, aes(x = xvalue, y = yvalue), fill = "grey", alpha = 0.2) +
coord_cartesian(ylim = c(ymin_StrawCheG29, ymax_StrawCheG29),
xlim = c(ymin_StrawCheG29, ymax_StrawCheG29)) +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Strawberry vs. Cherry") +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(16, 17)) +
scale_color_manual(values = c("#BC3C6D", "#3FAA96")) +
theme_LO_sober
StrawChe_G29
legend_tradevide <- ggplot(data = data_logchange[data_logchange$Generation == "29",],
aes(x =logchange, y = logchange, color = Symp,fill = Symp, shape = Allop)) +
geom_point(size =2.5, fill = "white") +
labs(shape = "Test fruit", color = "Selection fruit") +
scale_shape_manual(values = c(16, 15, 17)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober
legend_trade <- lemon::g_legend(legend_tradevide)
Slopeestimates_logchange_pairwise_errorbar <- cowplot::ggdraw() +
cowplot::draw_plot(CheCran_G7 + theme(legend.position = "none"),
x = 0.00, y = 0.5, width = 0.22, height = 0.45) +
cowplot::draw_plot(CranStraw_G7 + theme(legend.position = "none"),
x = 0.26, y = 0.5, width = 0.22, height = 0.45) +
cowplot::draw_plot(StrawChe_G7 + theme(legend.position = "none"),
x = 0.52, y = 0.5, width = 0.22, height = 0.45) +
cowplot::draw_plot(legend_trade, x = 0.750, y = 0.5, width = 0.1, height = 0.1) +
cowplot::draw_plot(CheCran_G29 + theme(legend.position = "none"),
x = 0.00, y = 0, width = 0.22, height = 0.45) +
cowplot::draw_plot(CranStraw_G29 + theme(legend.position = "none"),
x = 0.26, y = 0, width = 0.22, height = 0.45) +
cowplot::draw_plot(StrawChe_G29 + theme(legend.position = "none"),
x = 0.52, y = 0, width = 0.22, height = 0.45) +
cowplot::draw_plot_label(c("Intermediate phenotyping step", "A", "B", "C", " ",
"Final phenotyping step", "D", "E", "F", " "),
x = c(0.26, 0, 0.26, 0.52, 0.82, 0.2, 0, 0.26, 0.52, 0.82),
y = c(1, 0.95, 0.95, 0.95, 0.95, 0.5, 0.45, 0.45, 0.45, 0.45),
hjust = c(-0.25, -0.25, -0.25, -0.25, -0.25, -0.75, -0.75, -0.75, -0.75, -0.75),
vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
size = 14)
Slopeestimates_logchange_pairwise_errorbar
#
# #
#
cowplot::save_plot(file =here::here("figures", "FIG_MAregression.pdf"),
Slopeestimates_logchange_pairwise_errorbar,
base_height = 15/cm(1), base_width = 30/cm(1), dpi = 610)